`GridSearchCV` with custom estimator and nested Parameter Grids raises `ValueError` in scikit-learn 1.5.0

### Describe the bug

When using `GridSearchCV` with a custom estimator that includes nested parameter grids, a `ValueError` is raised in scikit-learn 1.5.0 indicating "entry not a 2- or 3- tuple". This issue does not occur in scikit-learn 1.4.0, where the grid search completes successfully.

### Steps/Code to Reproduce

```python
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator, ClassifierMixin

class SimpleEstimator(BaseEstimator, ClassifierMixin):
    def __init__(self, base_clf, param1=None, param2=True):
        self.base_clf = base_clf
        self.param1 = param1
        self.param2 = param2

    def fit(self, X, y=None):
        # Simulate using the parameters in the fitting process
        if self.param1:
            pass  # Simulate using param1
        if self.param2:
            pass  # Simulate using param2
        
        self.base_clf.fit(X, y)
        return self

    def predict(self, X):
        return self.base_clf.predict(X)

    def score(self, X, y):
        return self.base_clf.score(X, y)

def test_gridsearchcv_with_custom_estimator():
    param_grid = {
        "param1": [None, {"option": "A"}, {"option": "B"}],
        "param2": [True, False],
    }

    base_clf = LogisticRegression()

    grid_search = GridSearchCV(
        estimator=SimpleEstimator(base_clf),
        param_grid=param_grid,
        cv=3,
    )

    X_train = np.random.rand(20, 2)
    y_train = np.random.randint(0, 2, 20)

    grid_search.fit(X_train, y_train)
    print("Best params:", grid_search.best_params_)
    print("Best score:", grid_search.best_score_)

test_gridsearchcv_with_custom_estimator()
```

### Expected Results

The `GridSearchCV` should complete without any errors, exploring all combinations of the parameters specified in param_grid.

Example:

```
Best params: {'param1': None, 'param2': True}
Best score: 0.5
```

### Actual Results

In scikit-learn 1.5.0, the following error occurs:


```python
Traceback (most recent call last):
  File "/workspaces/mwe.py", line 49, in <module>
    test_gridsearchcv_with_custom_estimator()
  File "/workspaces/mwe.py", line 45, in test_gridsearchcv_with_custom_estimator
    grid_search.fit(X_train, y_train)
  File "/home/vscode/.local/lib/python3.11/site-packages/sklearn/base.py", line 1473, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py", line 968, in fit
    self._run_search(evaluate_candidates)
  File "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py", line 1543, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py", line 962, in evaluate_candidates
    results = self._format_results(
              ^^^^^^^^^^^^^^^^^^^^^
  File "/home/vscode/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py", line 1092, in _format_results
    arr_dtype = np.result_type(*param_list)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<__array_function__ internals>", line 200, in result_type
  File "/home/vscode/.local/lib/python3.11/site-packages/numpy/core/_internal.py", line 61, in _usefields
    names, formats, offsets, titles = _makenames_list(adict, align)
                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/vscode/.local/lib/python3.11/site-packages/numpy/core/_internal.py", line 31, in _makenames_list
    raise ValueError("entry not a 2- or 3- tuple")
ValueError: entry not a 2- or 3- tuple
```

In scikit-learn 1.4.0, the grid search completes successfully:

```
Best params: {'param1': None, 'param2': True}
Best score: 0.5
```

### Versions

```shell
System:
    python: 3.11.7 (main, Dec 19 2023, 20:33:49) [GCC 12.2.0]
executable: /usr/local/bin/python
   machine: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.36

Python dependencies:
      sklearn: 1.5.0
          pip: 23.2.1
   setuptools: 69.0.3
        numpy: 1.24.3
        scipy: 1.12.0
       Cython: None
       pandas: 2.2.0
   matplotlib: 3.7.4
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 32
         prefix: libopenblas
       filepath: /home/vscode/.local/lib/python3.11/site-packages/numpy.libs/libopenblas64_p-r0-15028c96.3.21.so
        version: 0.3.21
threading_layer: pthreads
   architecture: Zen

       user_api: blas
   internal_api: openblas
    num_threads: 32
         prefix: libopenblas
       filepath: /home/vscode/.local/lib/python3.11/site-packages/scipy.libs/libopenblasp-r0-23e5df77.3.21.dev.so
        version: 0.3.21.dev
threading_layer: pthreads
   architecture: Zen

       user_api: openmp
   internal_api: openmp
    num_threads: 32
         prefix: libgomp
       filepath: /home/vscode/.local/lib/python3.11/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
```

COMMENT:
This is likely due to https://github.com/scikit-learn/scikit-learn/pull/28352 that is using more specific dtypes for `.cv_results_`. I will open a PR for this. Edit: done in https://github.com/scikit-learn/scikit-learn/pull/29078

(edit: crossed message with @adrinjalali :wink:)

Labels: Bug, Regression