HistGradientBoostingClassifier raise error with monotonic constraints and categorical features

### Describe the bug

Creating an HistGradientBoostingClassifier with _monotonic_cst_ and _categorical_features_ is not possible because it throws an error. The _monotonic_cst_ is a numeric feature that is not included in the categorical features.

### Steps/Code to Reproduce

```python
from sklearn.datasets import fetch_openml
from sklearn.ensemble import HistGradientBoostingClassifier

X_adult, y_adult = fetch_openml("adult", version=2, return_X_y=True)
X_adult = X_adult[["age", "workclass", "education"]]
print(X_adult.dtypes)
# age             int64
# workclass    category
# education    category
# dtype: object

hist = HistGradientBoostingClassifier(
    monotonic_cst={"age": 1}, categorical_features="from_dtype"
)
hist.fit(X_adult, y_adult)
```
> ValueError: Categorical features cannot have monotonic constraints.

```python
hist = HistGradientBoostingClassifier(
    monotonic_cst={"age": 1}, categorical_features=["workclass", "education"]
)
hist.fit(X_adult, y_adult)
```
> ValueError: Categorical features cannot have monotonic constraints.
```

### Expected Results

The expected result will be a fitted model

### Actual Results

```python
{
    "name": "ValueError",
    "message": "Categorical features cannot have monotonic constraints.",
    "stack": "---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[13], line 10
      6 print(X_adult.dtypes)
      7 hist = HistGradientBoostingClassifier(
      8     monotonic_cst={\"age\": 1}, categorical_features=\"from_dtype\"
      9 )
---> 10 hist.fit(X_adult, y_adult)
     12 hist = HistGradientBoostingClassifier(
     13     monotonic_cst={\"age\": 1}, categorical_features=[\"workclass\", \"education\"]
     14 )
     15 hist.fit(X_adult, y_adult)

File ~/Projects/your_project/.venv/lib/python3.10/site-packages/sklearn/base.py:1474, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1467     estimator._validate_params()
   1469 with config_context(
   1470     skip_parameter_validation=(
   1471         prefer_skip_nested_validation or global_skip_validation
   1472     )
   1473 ):
-> 1474     return fit_method(estimator, *args, **kwargs)

File ~/Projects/your_project/.venv/lib/python3.10/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:889, in BaseHistGradientBoosting.fit(self, X, y, sample_weight)
    887 # Build `n_trees_per_iteration` trees.
    888 for k in range(self.n_trees_per_iteration_):
--> 889     grower = TreeGrower(
    890         X_binned=X_binned_train,
    891         gradients=g_view[:, k],
    892         hessians=h_view[:, k],
    893         n_bins=n_bins,
    894         n_bins_non_missing=self._bin_mapper.n_bins_non_missing_,
    895         has_missing_values=has_missing_values,
    896         is_categorical=self._is_categorical_remapped,
    897         monotonic_cst=monotonic_cst,
    898         interaction_cst=interaction_cst,
    899         max_leaf_nodes=self.max_leaf_nodes,
    900         max_depth=self.max_depth,
    901         min_samples_leaf=self.min_samples_leaf,
    902         l2_regularization=self.l2_regularization,
    903         feature_fraction_per_split=self.max_features,
    904         rng=self._feature_subsample_rng,
    905         shrinkage=self.learning_rate,
    906         n_threads=n_threads,
    907     )
    908     grower.grow()
    910     acc_apply_split_time += grower.total_apply_split_time

File ~/Projects/your_project/.venv/lib/python3.10/site-packages/sklearn/ensemble/_hist_gradient_boosting/grower.py:300, in TreeGrower.__init__(self, X_binned, gradients, hessians, max_leaf_nodes, max_depth, min_samples_leaf, min_gain_to_split, min_hessian_to_split, n_bins, n_bins_non_missing, has_missing_values, is_categorical, monotonic_cst, interaction_cst, l2_regularization, feature_fraction_per_split, rng, shrinkage, n_threads)
    293     is_categorical = np.asarray(is_categorical, dtype=np.uint8)
    295 if np.any(
    296     np.logical_and(
    297         is_categorical == 1, monotonic_cst != MonotonicConstraint.NO_CST
    298     )
    299 ):
--> 300     raise ValueError(\"Categorical features cannot have monotonic constraints.\")
    302 hessians_are_constant = hessians.shape[0] == 1
    303 self.histogram_builder = HistogramBuilder(
    304     X_binned, n_bins, gradients, hessians, hessians_are_constant, n_threads
    305 )

ValueError: Categorical features cannot have monotonic constraints."
}
```

### Versions

```shell
System:
    python: 3.10.13 (main, Apr  9 2024, 09:36:37) [Clang 15.0.0 (clang-1500.3.9.4)]
executable: /Users/user/Projects/your-project/.venv/bin/python
   machine: macOS-14.4.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.4.2
          pip: 24.0
   setuptools: 69.2.0
        numpy: 1.26.4
        scipy: 1.13.0
       Cython: None
       pandas: 2.2.1
   matplotlib: 3.8.4
       joblib: 1.4.0
threadpoolctl: 3.4.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
    num_threads: 10
         prefix: libomp
       filepath: /Users/user/Projects/your-project/.venv/lib/python3.10/site-packages/sklearn/.dylibs/libomp.dylib
        version: None

       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: /Users/user/Projects/your-project/.venv/lib/python3.10/site-packages/numpy/.dylibs/libopenblas64_.0.dylib
        version: 0.3.23.dev
threading_layer: pthreads
   architecture: armv8

       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: /Users/user/Projects/your-project/.venv/lib/python3.10/site-packages/scipy/.dylibs/libopenblas.0.dylib
        version: 0.3.26.dev
threading_layer: pthreads
   architecture: neoversen1
```

COMMENT:
I digged into a bit. The problem is that during fitting, `ColumnTransformer` places the categorical features at the beginning of X, but `monotonic_cst` still keeps the original order of the features. I think we can remap `monotonic_cst` to fix the issue, sort of like `self._is_categorical_remapped`.

Labels: Bug