KNeighborsRegressor with metric="nan_euclidean" does not actually support NaN values

### Describe the bug

[KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) claims to support [these distance metrics](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.distance_metrics.html), which includes one called `"nan_euclidean"`, which presumably refers to [this metric](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html), which calculates "the euclidean distances in the presence of missing values." So, using this metric in `KNeighborsRegressor` should allow it to find nearest neighbors even in the presence of missing (NaN) values.

However, after setting `metric="nan_euclidean"`, the `fit()` method raises an error complaining that the "Input contains NaN". But this should not be an error, because I've chosen a distance metric that supports NaN values.

### Steps/Code to Reproduce

```py
X = [[0, 1], [1, np.nan], [2, 3,], [3, 5]]
y = [0, 0, 1, 1]
from sklearn.neighbors import KNeighborsRegressor
neigh = KNeighborsRegressor(n_neighbors=2, metric="nan_euclidean")
neigh.fit(X, y)
```

### Expected Results

No error is thrown.

### Actual Results

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-227-07ade1a53cbc>](https://localhost:8080/#) in <module>
      3 from sklearn.neighbors import KNeighborsRegressor
      4 neigh = KNeighborsRegressor(n_neighbors=2, metric="nan_euclidean")
----> 5 neigh.fit(X, y)
      6 neigh.predict([[1.5, 3]])

5 frames
[/usr/local/lib/python3.8/dist-packages/sklearn/neighbors/_regression.py](https://localhost:8080/#) in fit(self, X, y)
    211         self.weights = _check_weights(self.weights)
    212 
--> 213         return self._fit(X, y)
    214 
    215     def predict(self, X):

[/usr/local/lib/python3.8/dist-packages/sklearn/neighbors/_base.py](https://localhost:8080/#) in _fit(self, X, y)
    398         if self._get_tags()["requires_y"]:
    399             if not isinstance(X, (KDTree, BallTree, NeighborsBase)):
--> 400                 X, y = self._validate_data(X, y, accept_sparse="csr", multi_output=True)
    401 
    402             if is_classifier(self):

[/usr/local/lib/python3.8/dist-packages/sklearn/base.py](https://localhost:8080/#) in _validate_data(self, X, y, reset, validate_separately, **check_params)
    579                 y = check_array(y, **check_y_params)
    580             else:
--> 581                 X, y = check_X_y(X, y, **check_params)
    582             out = X, y
    583 

[/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py](https://localhost:8080/#) in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    962         raise ValueError("y cannot be None")
    963 
--> 964     X = check_array(
    965         X,
    966         accept_sparse=accept_sparse,

[/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py](https://localhost:8080/#) in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    798 
    799         if force_all_finite:
--> 800             _assert_all_finite(array, allow_nan=force_all_finite == "allow-nan")
    801 
    802     if ensure_min_samples > 0:

[/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py](https://localhost:8080/#) in _assert_all_finite(X, allow_nan, msg_dtype)
    112         ):
    113             type_err = "infinity" if allow_nan else "NaN, infinity"
--> 114             raise ValueError(
    115                 msg_err.format(
    116                     type_err, msg_dtype if msg_dtype is not None else X.dtype

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
```

### Versions

```shell
System:
    python: 3.8.16 (default, Dec  7 2022, 01:12:13)  [GCC 7.5.0]
executable: /usr/bin/python3
   machine: Linux-5.10.147+-x86_64-with-glibc2.27

Python dependencies:
          pip: 22.0.4
   setuptools: 57.4.0
      sklearn: 1.0.2
        numpy: 1.21.6
        scipy: 1.7.3
       Cython: 0.29.32
       pandas: 1.3.5
   matplotlib: 3.2.2
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```
```

COMMENT:
With custom stuff, I am wondering if it would not be best to use `set_config` or `config_context` and the dedicated `assume_finite` parameter to make deactivate the check then.

```python
with sklearn.config_context(assume_finite=False):
    KNeighborsRegressor().fit(X, y)  # no check for finiteness
```

If we want to be consistent and use the `supports_nan`, we would need to add it in all estimators while the config allows such usage.

Labels: Bug, module:neighbors