IterableDataset: corrupted EXIF image silently terminates streaming iterator instead of skipping the sample

## Summary

When iterating over a streaming `IterableDataset` that contains images with corrupted EXIF metadata (e.g., a TIFF rational tag with denominator=0), PIL raises `ZeroDivisionError` inside `exif_transpose`. This exception propagates through the HuggingFace `datasets` pipeline and **terminates the streaming iterator** — the caller cannot catch it and resume from the next sample.

## Environment

- `datasets` version: 4.4.1
- `Pillow` version: 10.x
- Dataset: CC12M (WebDataset format, loaded as streaming `IterableDataset`)

## Minimal Reproducer

```python
from datasets import load_dataset

ds = load_dataset("pixparse/cc12m-wds", split="train", streaming=True)
it = iter(ds)
for i in range(10_000):
try:
sample = next(it)
except ZeroDivisionError as e:
print(f"sample {i}: {e}")
# Try to resume — this raises StopIteration immediately,
# because the internal generator's frame is already closed.
next(it)
break
```

Expected: skip the bad sample and continue.
Actual: `StopIteration` on the next call — iterator is dead.

## Traceback

```
File "PIL/ImageOps.py", line 711, in exif_transpose
File "PIL/TiffImagePlugin.py", line 297, in _limit_rational
ZeroDivisionError: division by zero
```

## Root Cause

`PIL.ImageOps.exif_transpose` does not guard against rational EXIF tags with denominator=0, which exist in real-world web-crawled datasets. Inside `datasets`, the call chain is roughly:

- `IterableDataset.__iter__` → `_iter_arrow` / `_iter_pytorch` →
- `Image.decode_example()` (in `datasets/features/image.py`) →
- `PIL.Image.open(...)` + `PIL.ImageOps.exif_transpose(image)` ← raises here

Because the exception is raised inside the internal generator, the generator's frame is closed when it propagates out of `__next__`, so calling `next()` again raises `StopIteration`.

## Impact on Training Frameworks

For training frameworks using streaming datasets, none of the current workarounds is acceptable:

1. **Recreate the iterator** after the exception → restarts from sample 0, causing silent data repetition (the entire dataset is re-looped from the beginning).
2. **Continue without recreating** → raises `StopIteration` immediately, ends iteration.
3. **Monkey-patch PIL globally** → invasive and hides data quality issues from the user.

This was discussed in [pytorch/torchtitan#2550](https://github.com/pytorch/torchtitan/pull/2550), where the reviewer (`@wwwjn`) explicitly recommended that the proper fix should live in `datasets`, not in the training framework.

## Requested Fix

Add a `skip_corrupted_images: bool = False` option (or similar) to `load_dataset()` / `IterableDataset`.

- **`skip_corrupted_images=False` (default):** preserve current behavior — let the exception propagate so users are aware of data quality issues.
- **`skip_corrupted_images=True`:** catch decode errors **inside** the image feature decoder, emit a `UserWarning` (or expose a counter), and continue to the next sample without terminating the iterator.

**Note:** the `skip_corrupted_images=True` behavior **requires** the catch to happen inside the image decoder. Without catching the error before it propagates out of the generator, there is no way to skip the corrupted sample and resume — the iterator is already terminated at that point.

## Open design questions for maintainers

A few things I'd like your guidance on before sending a PR:

1. **API surface**: a single `skip_corrupted_images: bool` flag, or a more general `on_decode_error: Literal["raise", "skip"] = "raise"` so audio/video features can reuse it later?
2. **Exception scope**: catch only `(ZeroDivisionError, PIL.UnidentifiedImageError, OSError)`, or any `Exception` raised from `decode_example`?
3. **Visibility**: `UserWarning` per skip, or a single warning + a counter exposed on the dataset object (e.g. `ds.num_skipped`)?
4. **Layer**: implement inside `Image.decode_example`, or one level up in `IterableDataset._iter_*` so it works uniformly across feature types?

Happy to follow whichever direction you prefer.

## Contribution

I'd be happy to submit a PR implementing this once we've agreed on the design above. Please let me know which option you prefer and I'll get started.