sample_weight is silently ignored in LogisticRegressionCV.score when metadata routing is enabled

### Describe the bug

I'm not sure if it is a proper bug, or my lack of understanding of the metadata routing API ;)

When `enable_metadata_routing=True`, the `score` method of a `LogisticRegressionCV` estimator will ignore `sample_weight`.
```python
set_config(enable_metadata_routing=True)
logreg_cv = LogisticRegressionCV().fit(X, y)
logreg_cv.score(X, y, sample_weight=sw)==logreg_cv.score(X, y) #unweighted accuracy
```
I found it surprising, because the `score` method works fine when `enable_metadata_routing=False`, so the same piece of code behaves differently depending on the metadata routing config.
```python
set_config(enable_metadata_routing=False)
logreg_cv = LogisticRegressionCV().fit(X, y)
logreg_cv.score(X, y, sample_weight=sw) #weighted accuracy
```

If I understood the metadata routing API correctly, to make the `score` method `sample_weight` aware we need to explicitly pass a scorer that request it:
```python
set_config(enable_metadata_routing=True)
weighted_accuracy = make_scorer(accuracy_score).set_score_request(sample_weight=True)
logreg_cv = LogisticRegressionCV(scoring=weighted_accuracy).fit(X, y)
logreg_cv.score(X, y, sample_weight=sw) #weighted accuracy
```

If it's the intended behavior of the metadata routing API, maybe we should warn the user or raise an error in the first case, instead of silently ignoring `sample_weight` ?

### Steps/Code to Reproduce

```python
from sklearn import set_config
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.linear_model import LogisticRegressionCV
import numpy as np

rng = np.random.RandomState(22)
n_samples, n_features = 10, 4
X = rng.rand(n_samples, n_features)
y = rng.randint(0, 2, size=n_samples)
sw = rng.randint(0, 5, size=n_samples)

set_config(enable_metadata_routing=True)
logreg_cv = LogisticRegressionCV()
logreg_cv.fit(X, y)
# sample_weight is silently ignored in logreg_cv.score
assert logreg_cv.score(X, y) == logreg_cv.score(X, y, sample_weight=sw) 
assert not logreg_cv.score(X, y, sample_weight=sw)==accuracy_score(logreg_cv.predict(X),y, sample_weight=sw)
```

### Expected Results

Either `logreg_cv.score(X, y, sample_weight=sw)` raises an error/warning or the assertions are false.

### Actual Results

The assertions are true.

### Versions

```shell
sklearn: 1.7.dev0
```

COMMENT:
A change is also needed in `test_lr_cv_scores_differ_when_sample_weight_is_requested`.  Currently, as the description states, that test looks for different behavior in the `.fit` and `.score` methods when sample_weight is requested. However, the arguments passed to both scorers are the same (including `sample_weight`).  Due to the current bug, the test succeeds, but the actual testing scenario should involve different scorers: one with `sample_weight` and the other without it. This is necessary to verify that `sample_weight` is correctly passed.

```python
@config_context(enable_metadata_routing=True)`
def test_lr_cv_scores_differ_when_sample_weight_is_requested():
    """Test that `sample_weight` is correctly passed to the scorer in
    `LogisticRegressionCV.fit` and `LogisticRegressionCV.score` by
    checking the difference in scores with the case when `sample_weight`
    is not requested.
    """
    rng = np.random.RandomState(10)
    X, y = make_classification(n_samples=10, random_state=rng)
    X_t, y_t = make_classification(n_samples=10, random_state=rng)
    sample_weight = np.ones(len(y))
    sample_weight[: len(y) // 2] = 2
    kwargs = {"sample_weight": sample_weight}

    scorer1 = get_scorer("accuracy")
    lr_cv1 = LogisticRegressionCV(scoring=scorer1)
    lr_cv1.fit(X, y) # Don't pass the sample_weight, we are testing differences, we cant pass the same arguments in both scorers

    scorer2 = get_scorer("accuracy")
    scorer2.set_score_request(sample_weight=True)
    lr_cv2 = LogisticRegressionCV(scoring=scorer2)
    lr_cv2.fit(X, y, **kwargs)

    assert not np.allclose(lr_cv1.scores_[1], lr_cv2.scores_[1])

    score_1 = lr_cv1.score(X_t, y_t) # Same here, don't pass sample_weight for test differences
    score_2 = lr_cv2.score(X_t, y_t, **kwargs)

    assert not np.allclose(score_1, score_2)
```

Labels: Bug, Metadata Routing