MinMaxScaler is not array API compliant if clip=True

### Describe the bug

The `MinMaxScaler` is [listed](https://github.com/scikit-learn/scikit-learn/blob/21e1642b0b47475ffd476c9df6c9984d71b90b1d/doc/modules/array_api.rst#estimators) as array API compliant, but uses non compliant code under some configuration. Namely, it [calls](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/preprocessing/_data.py#L540) `clip` ([link](https://data-apis.org/array-api/draft/API_specification/generated/array_api.clip.html#clip) to standard) with the non-standard `out` kwarg [here](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/preprocessing/_data.py#L540).

While this particular fix may be simple, I am a bit unsure how to best test for array API compliance. The `array-api-strict` package is only implemented up to version `2022.12` of the standard which didn't include `clip` yet. I actually discovered the bug using the lazy [`ndonnx`](https://github.com/Quantco/ndonnx/tree/main/ndonnx) package, but it does not serve well as a reproducer here since I also run into other, earlier, issues related to eager data validation.

### Steps/Code to Reproduce

```python
from sklearn.preprocessing import MinMaxScaler
import array_api_strict as xp
import numpy as np
import sklearn

def test_minmax_scaler_clip():
    feature_range = (0, 1)
    # test behaviour of the parameter 'clip' in MinMaxScaler
    # X = iris.data
    X = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
    scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(X)
    X_min, X_max = np.min(X, axis=0), np.max(X, axis=0)
    X_test = xp.asarray([np.r_[X_min[:2] - 10, X_max[2:] + 10]])
    X_transformed = scaler.transform(X_test)


with sklearn.config_context(array_api_dispatch=True):
    test_minmax_scaler_clip()
```

### Expected Results

I expected that `MinMaxScaler.transform` would just work with a standard compliant implementation.

### Actual Results

The `array-api-strict` package is lagging behind and fails with the notice that `clip` is not implemented, yet.
```
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[6], line 2
      1 with sklearn.config_context(array_api_dispatch=True):
----> 2     test_minmax_scaler_clip()

Cell In[5], line 9, in test_minmax_scaler_clip()
      7 X_min, X_max = np.min(X, axis=0), np.max(X, axis=0)
      8 X_test = xp.asarray([np.r_[X_min[:2] - 10, X_max[2:] + 10]])
----> 9 X_transformed = scaler.transform(X_test)

File [~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/sklearn/utils/_set_output.py:313](http://localhost:8889/lab/tree/~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/sklearn/utils/_set_output.py#line=312), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    311 @wraps(f)
    312 def wrapped(self, X, *args, **kwargs):
--> 313     data_to_wrap = f(self, X, *args, **kwargs)
    314     if isinstance(data_to_wrap, tuple):
    315         # only wrap the first output for cross decomposition
    316         return_tuple = (
    317             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    318             *data_to_wrap[1:],
    319         )

File [~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/sklearn/preprocessing/_data.py:546](http://localhost:8889/lab/tree/~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/sklearn/preprocessing/_data.py#line=545), in MinMaxScaler.transform(self, X)
    544 X += self.min_
    545 if self.clip:
--> 546     xp.clip(X, self.feature_range[0], self.feature_range[1], out=X)
    547 return X

File [~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/array_api_strict/_flags.py:355](http://localhost:8889/lab/tree/~/micromamba/envs/strict-array-api/lib/python3.12/site-packages/array_api_strict/_flags.py#line=354), in requires_api_version.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    352 @functools.wraps(func)
    353 def wrapper(*args, **kwargs):
    354     if version > API_VERSION:
--> 355         raise RuntimeError(
    356             f"The function {func.__name__} requires API version {version} or later, "
    357             f"but the current API version for array-api-strict is {API_VERSION}"
    358         )
    359     return func(*args, **kwargs)

RuntimeError: The function clip requires API version 2023.12 or later, but the current API version for array-api-strict is 2022.12
```

### Versions

```shell
System:
    python: 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:13:44) [Clang 16.0.6 ]
executable: /Users/c.bourjau/micromamba/envs/strict-array-api/bin/python
   machine: macOS-14.2.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.5.1
          pip: 24.2
   setuptools: 71.0.4
        numpy: 2.0.1
        scipy: 1.14.0
       Cython: None
       pandas: None
   matplotlib: None
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libopenblas
       filepath: /Users/c.bourjau/micromamba/envs/strict-array-api/lib/libopenblas.0.dylib
        version: 0.3.27
threading_layer: openmp
   architecture: VORTEX

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: libomp
       filepath: /Users/c.bourjau/micromamba/envs/strict-array-api/lib/libomp.dylib
        version: None
```

COMMENT:
> One question though: I am not sure to which extent with want to test estimators for array API compat with non-default hparams. This can quickly lead to a combinatorial explosion of test cases.

Indeed! I believe this may be most practically with static type checkers. Unfortunatelly, there is no `Protocl` for the API, yet. There is a very well develop yet seemingly dormant [PR](https://github.com/data-apis/array-api/pull/589) for that, though.

Labels: Bug, module:preprocessing, Array API