cast_column to ClassLabel silently accepts out-of-range label indices

### Describe the bug

`Dataset.cast_column` / `Dataset.cast` to a `ClassLabel` column silently keeps label indices that are out of range for the target number of classes. The corruption only shows up later — e.g. on the first `int2str` call — or silently trains on wrong labels:

```python
from datasets import Dataset, ClassLabel

d = Dataset.from_dict({"label": [5, 1]})
casted = d.cast_column("label", ClassLabel(names=["neg", "pos", "oth"])) # no error, num_classes=3
print(casted["label"]) # [5, 1]
casted.features["label"].int2str(5) # ValueError: Invalid integer class label 5
```

The write path does validate the same data — `Dataset.from_dict(..., features=Features({"label": ClassLabel(names=[...])}))` raises `ValueError: Class label 5 greater than configured num_classes 3` — so the cast path bypasses a check the writer performs.

Cause: `table_cast` in `src/datasets/table.py` only runs the feature-level casts when the arrow schema changes, and `pa.Schema.__eq__` ignores metadata. Casting int64 storage to `ClassLabel` leaves the arrow schema unchanged and only alters the metadata, so it takes the `replace_schema_metadata` branch and `ClassLabel.cast_storage`, which does the range check, never runs.

### Expected behavior

`cast_column`/`cast` should raise the same `ValueError` as the write path when a label index is `>= num_classes`.

Start in src/datasets/table.py at table_cast, then trace Dataset.cast_column and Dataset.cast. Compare this path with Dataset.from_dict(..., features=Features(...)) and the ClassLabel storage validation described in the issue. Done means casting out-of-range indices raises the same ValueError as the write path, with regression coverage for the shown example.

Written by the indexing model from the issue text.