`SimpleImputer` Fails with pyarrow String Types in sklearn

### Describe the bug

When using SimpleImputer from sklearn with pyarrow string types, the imputer fails with an error. This issue occurs when attempting to impute missing values in a DataFrame containing pyarrow string columns.

### Steps/Code to Reproduce

```python
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer

# Create a DataFrame with pyarrow string types
data = {
    'mpg': [18, 25, 120, 120],
    'make': ['Ford', 'Chevy', np.nan, 'Tesla']
}
df = (pd.DataFrame(data)
       .astype({'make':'string[pyarrow]'})
)

# Initialize SimpleImputer
imputer = SimpleImputer(strategy='most_frequent')

# Attempt to fit and transform the DataFrame
imputer.fit_transform(df[["make"]])
```


### Expected Results

The SimpleImputer should handle pyarrow string types and impute the missing values without raising an error.

### Actual Results

```
AttributeError                            Traceback (most recent call last)
Cell In[158], line 18
     15 imputer = SimpleImputer(strategy='most_frequent')
     17 # Attempt to fit and transform the DataFrame
---> 18 imputer.fit_transform(df[[\"make\"]])

File ~/.envs/menv/lib/python3.10/site-packages/sklearn/utils/_set_output.py:295, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    293 @wraps(f)
    294 def wrapped(self, X, *args, **kwargs):
--> 295     data_to_wrap = f(self, X, *args, **kwargs)
    296     if isinstance(data_to_wrap, tuple):
    297         # only wrap the first output for cross decomposition
    298         return_tuple = (
    299             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    300             *data_to_wrap[1:],
    301         )

File ~/.envs/menv/lib/python3.10/site-packages/sklearn/base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params)
   1083         warnings.warn(
   1084             (
   1085                 f\"This object ({self.__class__.__name__}) has a `transform`\"
   (...)
   1093             UserWarning,
   1094         )
   1096 if y is None:
   1097     # fit method of arity 1 (unsupervised transformation)
-> 1098     return self.fit(X, **fit_params).transform(X)
   1099 else:
   1100     # fit method of arity 2 (supervised transformation)
   1101     return self.fit(X, y, **fit_params).transform(X)

File ~/.envs/menv/lib/python3.10/site-packages/sklearn/base.py:1474, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1467     estimator._validate_params()
   1469 with config_context(
   1470     skip_parameter_validation=(
   1471         prefer_skip_nested_validation or global_skip_validation
   1472     )
   1473 ):
-> 1474     return fit_method(estimator, *args, **kwargs)

File ~/.envs/menv/lib/python3.10/site-packages/sklearn/impute/_base.py:427, in SimpleImputer.fit(self, X, y)
    423     self.statistics_ = self._sparse_fit(
    424         X, self.strategy, self.missing_values, fill_value
    425     )
    426 else:
--> 427     self.statistics_ = self._dense_fit(
    428         X, self.strategy, self.missing_values, fill_value
    429     )
    431 return self

File ~/.envs/menv/lib/python3.10/site-packages/sklearn/impute/_base.py:510, in SimpleImputer._dense_fit(self, X, strategy, missing_values, fill_value)
    503 elif strategy == \"most_frequent\":
    504     # Avoid use of scipy.stats.mstats.mode due to the required
    505     # additional overhead and slow benchmarking performance.
    506     # See Issue 14325 and PR 14399 for full discussion.
    507 
    508     # To be able access the elements by columns
    509     X = X.transpose()
--> 510     mask = missing_mask.transpose()
    512     if X.dtype.kind == \"O\":
    513         most_frequent = np.empty(X.shape[0], dtype=object)

AttributeError: 'bool' object has no attribute 'transpose'"
```

### Versions

```shell
System:
    python: 3.10.14 (main, Mar 19 2024, 21:46:16) [Clang 15.0.0 (clang-1500.3.9.4)]
executable: /Users/matt/.envs/menv/bin/python
   machine: macOS-14.2.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.4.1.post1
          pip: 24.0
   setuptools: 67.6.1
        numpy: 1.23.5
        scipy: 1.10.0
       Cython: 0.29.37
       pandas: 2.2.2
   matplotlib: 3.6.2
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/matt/.envs/menv/lib/python3.10/site-packages/numpy/.dylibs/libopenblas64_.0.dylib
        version: 0.3.20
threading_layer: pthreads
   architecture: armv8
    num_threads: 8

       user_api: openmp
   internal_api: openmp
         prefix: libomp
       filepath: /Users/matt/.envs/menv/lib/python3.10/site-packages/sklearn/.dylibs/libomp.dylib
        version: None
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/matt/.envs/menv/lib/python3.10/site-packages/scipy/.dylibs/libopenblas.0.dylib
        version: 0.3.18
threading_layer: pthreads
   architecture: armv8
    num_threads: 8
```

COMMENT:
@adrinjalali  ok thanks for the info. Most of the issues I saw had PRs linked and in open state. Does it mean it would be repeat effort to look into such issues

Labels: Bug, New Feature, Pandas compatibility