SimpleImputer's fill_value validation seems too strict

### Describe the bug

The `SimpleImputer` checks whether the _type_ of the `fill_value` can be cast with numpy to the dtype of the input data (`X`) using `np.can_cast(fill_value_dtype, X.dtype, casting="same_kind")`: https://github.com/scikit-learn/scikit-learn/blob/0ad90d51537328b7310741d010e569ca6cd33f78/sklearn/impute/_base.py#L397.

This seems too strict to me, and means one cannot impute a uint8 array with fill_value=0 (a python int). Replacing the validation with something like `np.can_cast(fill_value, X.dtype, casting="safe")` would be more permissible, and without knowing all the details looks safe enough, but perhaps I'm missing something.

### Steps/Code to Reproduce

Though the example in isolation doesn't make a lot of sense (imputing data that doesn't contain missing values), this case might occur with generically defined transformation pipelines applied to unknown datasets.

``` python
import pandas as pd
from sklearn import impute

df = pd.Series([0, 1, 2], dtype="uint8").to_frame()
impute.SimpleImputer(strategy="constant", fill_value=0).fit_transform(df)
```


### Expected Results

```
array([[0],
       [1],
       [2]], dtype=int8)
```

### Actual Results

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[19], [line 5](vscode-notebook-cell:?execution_count=19&line=5)
      [2](vscode-notebook-cell:?execution_count=19&line=2) from sklearn import impute
      [4](vscode-notebook-cell:?execution_count=19&line=4) df = pd.Series([0, 1, 2], dtype="uint8").to_frame()
----> [5](vscode-notebook-cell:?execution_count=19&line=5) impute.SimpleImputer(strategy="constant", fill_value=0).fit_transform(df)

File ~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:295, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [293](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:293) @wraps(f)
    [294](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:294) def wrapped(self, X, *args, **kwargs):
--> [295](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:295)     data_to_wrap = f(self, X, *args, **kwargs)
    [296](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:296)     if isinstance(data_to_wrap, tuple):
    [297](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:297)         # only wrap the first output for cross decomposition
    [298](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:298)         return_tuple = (
    [299](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:299)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [300](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:300)             *data_to_wrap[1:],
    [301](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/utils/_set_output.py:301)         )

File ~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params)
   [1083](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1083)         warnings.warn(
   [1084](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1084)             (
   [1085](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1085)                 f"This object ({self.__class__.__name__}) has a `transform`"
   (...)
   [1093](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1093)             UserWarning,
   [1094](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1094)         )
   [1096](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1096) if y is None:
   [1097](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1097)     # fit method of arity 1 (unsupervised transformation)
-> [1098](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1098)     return self.fit(X, **fit_params).transform(X)
   [1099](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1099) else:
   [1100](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1100)     # fit method of arity 2 (supervised transformation)
   [1101](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1101)     return self.fit(X, y, **fit_params).transform(X)

File ~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1474, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   [1467](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1467)     estimator._validate_params()
   [1469](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1469) with config_context(
   [1470](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1470)     skip_parameter_validation=(
   [1471](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1471)         prefer_skip_nested_validation or global_skip_validation
   [1472](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1472)     )
   [1473](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1473) ):
-> [1474](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/base.py:1474)     return fit_method(estimator, *args, **kwargs)

File ~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:410, in SimpleImputer.fit(self, X, y)
    [392](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:392) @_fit_context(prefer_skip_nested_validation=True)
    [393](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:393) def fit(self, X, y=None):
    [394](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:394)     """Fit the imputer on `X`.
    [395](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:395) 
    [396](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:396)     Parameters
   (...)
    [408](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:408)         Fitted estimator.
    [409](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:409)     """
--> [410](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:410)     X = self._validate_input(X, in_fit=True)
    [412](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:412)     # default fill_value is 0 for numerical input and "missing_value"
    [413](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:413)     # otherwise
    [414](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:414)     if self.fill_value is None:

File ~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:388, in SimpleImputer._validate_input(self, X, in_fit)
    [386](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:386)     # Make sure we can safely cast fill_value dtype to the input data dtype
    [387](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:387)     if not np.can_cast(fill_value_dtype, X.dtype, casting="same_kind"):
--> [388](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:388)         raise ValueError(err_msg)
    [390](https://file+.vscode-resource.vscode-cdn.net/Users/thomas/code/notebooks/~/micromamba/envs/grapy/lib/python3.9/site-packages/sklearn/impute/_base.py:390) return X

ValueError: fill_value=0 (of type <class 'int'>) cannot be cast to the input data that is dtype('uint8'). Make sure that both dtypes are of the same kind.
```

### Versions

```shell
System:
    python: 3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:55:20)  [Clang 16.0.6 ]
executable: /Users/thomas/micromamba/envs/grapy/bin/python
   machine: macOS-14.4.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.4.2
          pip: 24.0
   setuptools: 65.5.1
        numpy: 1.26.4
        scipy: 1.11.3
       Cython: 0.29.37
       pandas: 1.5.3
   matplotlib: 3.8.4
       joblib: 1.4.0
threadpoolctl: 3.4.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libopenblas
       filepath: /Users/thomas/micromamba/envs/grapy/lib/libopenblas.0.dylib
        version: 0.3.25
threading_layer: openmp
   architecture: VORTEX

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: libomp
       filepath: /Users/thomas/micromamba/envs/grapy/lib/libomp.dylib
        version: None
```

COMMENT:
was brainstorming potential solutions for this issue, was trying to use @lesteve 's second option but i think there is no clean way to convert python scalars to a dtype which is not known in advance, here is a relevant [stack overflow thread](https://stackoverflow.com/questions/20805087/create-a-numpy-scalar-from-dtype). perhaps i am missing something? 

in any case i think it would make for a nicer user experience if we took either of the two options presented above, and would be interested in helping to write something.

**edit: will probably not look at this. for anyone wants to take this issue, feel free**

Labels: Easy, Enhancement