[BUG] DaskXGBClassifier raises ValueError when scored with sklearn.metrics.get_scorer('roc_auc')

# Description

When using `sklearn.metrics.get_scorer("roc_auc")` on a `DaskXGBClassifier`, scikit-learn attempts to call `predict_proba` on what it believes is a regressor, triggering the error:

`ValueError: DaskXGBClassifier should either be a classifier to be used with
response_method=predict_proba or the response_method should be 'predict'.
Got a regressor with response_method=predict_proba instead.`

Manually creating a scorer with `make_scorer(roc_auc_score)` works fine, as do other built-in scorers that only need predict (e.g. "f1"). The bug does not occur for the non-Dask XGBClassifier.

## Examples

DaskXGBClassifier:

```python
import dask.dataframe as dd
import pandas as pd
import xgboost as xgb
from distributed import Client, LocalCluster
from sklearn.datasets import make_classification
from sklearn.metrics import get_scorer, make_scorer, roc_auc_score

c = Client(LocalCluster())
X, y = make_classification()
X = dd.from_array(X, columns=[f"var{i}" for i in range(X.shape[1])])
y = dd.from_array(y)

obj = xgb.dask.DaskXGBClassifier().fit(X, y)

auc_score = make_scorer(roc_auc_score)(obj, X, y) # works fine
f1 = get_scorer("f1")(obj, X, y) # works fine
print(get_scorer("roc_auc")(obj, X, y)) # raises ValueError
```

Raises the following exception:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[4], line 17
15 auc_score = make_scorer(roc_auc_score)(obj, X, y) # works fine
16 f1 = get_scorer("f1")(obj, X, y) # works fine
---> 17 print(get_scorer("roc_auc")(obj, X, y)) # raises ValueError

File [/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:288](http://127.0.0.1:8888/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py#line=287), in _BaseScorer.__call__(self, estimator, X, y_true, sample_weight, **kwargs)
285 if sample_weight is not None:
286 _kwargs["sample_weight"] = sample_weight
--> 288 return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)

File [/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:380](http://127.0.0.1:8888/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py#line=379), in _Scorer._score(self, method_caller, estimator, X, y_true, **kwargs)
378 pos_label = None if is_regressor(estimator) else self._get_pos_label()
379 response_method = _check_response_method(estimator, self._response_method)
--> 380 y_pred = method_caller(
381 estimator,
382 _get_response_method_name(response_method),
383 X,
384 pos_label=pos_label,
385 )
387 scoring_kwargs = {**self._kwargs, **kwargs}
388 return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)

File [/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:90](http://127.0.0.1:8888/usr/local/lib/python3.12/site-packages/sklearn/metrics/_scorer.py#line=89), in _cached_call(cache, estimator, response_method, *args, **kwargs)
87 if cache is not None and response_method in cache:
88 return cache[response_method]
---> 90 result, _ = _get_response_values(
91 estimator, *args, response_method=response_method, **kwargs
92 )
94 if cache is not None:
95 cache[response_method] = result

File [/usr/local/lib/python3.12/site-packages/sklearn/utils/_response.py:235](http://127.0.0.1:8888/usr/local/lib/python3.12/site-packages/sklearn/utils/_response.py#line=234), in _get_response_values(estimator, X, response_method, pos_label, return_response_method_used)
233 else: # estimator is a regressor
234 if response_method != "predict":
--> 235 raise ValueError(
236 f"{estimator.__class__.__name__} should either be a classifier to be "
237 f"used with response_method={response_method} or the response_method "
238 "should be 'predict'. Got a regressor with response_method="
239 f"{response_method} instead."
240 )
241 prediction_method = estimator.predict
242 y_pred, pos_label = prediction_method(X), None

ValueError: DaskXGBClassifier should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.
```

but the vanilla XGBClassifier is not affected:

```python
import pandas as pd
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.metrics import get_scorer, make_scorer, roc_auc_score

X, y = make_classification()
X = pd.DataFrame(X, columns=[f"var{i}" for i in range(X.shape[1])])
y = pd.Series(y)

obj = xgb.XGBClassifier().fit(X, y)

auc_score = make_scorer(roc_auc_score)(obj, X, y) # works fine
f1 = get_scorer("f1")(obj, X, y) # works fine
other_auc_score = get_scorer("roc_auc")(obj, X, y) # works fine
```

## Environment
dask==2024.8.0
xgboost==2.1.4
scikit-learn==1.6.1

system: python:3.12-slim-bullseye docker container on Mac M3