embed_table_storage crashes (SIGKILL) on sharded datasets with Sequence() nested types

## Summary

`embed_table_storage` crashes with SIGKILL (exit code 137) when processing sharded datasets containing `Sequence()` nested types like `Sequence(Nifti())`. Likely affects `Sequence(Image())` and `Sequence(Audio())` as well.

The crash occurs at the C++ level with no Python traceback.

### Related Issues

- #7852 - Problems with NifTI (closed, but related embedding issues)
- #6790 - PyArrow 'Memory mapping file failed' (potentially related)
- #7893 - OOM issue (separate bug, but discovered together)

### Context

Discovered while uploading the [Aphasia Recovery Cohort (ARC)](https://openneuro.org/datasets/ds004884) neuroimaging dataset to HuggingFace Hub. Even after fixing the OOM issue (#7893), this crash blocked uploads.

Working implementation with workaround: [arc-aphasia-bids](https://github.com/The-Obstacle-Is-The-Way/arc-aphasia-bids)

## Reproduction

```python
from datasets import Dataset, Features, Sequence, Value
from datasets.features import Nifti
from datasets.table import embed_table_storage

features = Features({
"id": Value("string"),
"images": Sequence(Nifti()),
})

ds = Dataset.from_dict({
"id": ["a", "b"],
"images": [["/path/to/file.nii.gz"], []],
}).cast(features)

# This works fine:
table = ds._data.table.combine_chunks()
embedded = embed_table_storage(table) # OK

# This crashes with SIGKILL:
shard = ds.shard(num_shards=2, index=0)
shard_table = shard._data.table.combine_chunks()
embedded = embed_table_storage(shard_table) # CRASH - no Python traceback
```

## Key Observations

| Scenario | Result |
|----------|--------|
| Single `Nifti()` column | Works |
| `Sequence(Nifti())` on full dataset | Works |
| `Sequence(Nifti())` after `ds.shard()` | **CRASHES** |
| `Sequence(Nifti())` after `ds.select([i])` | **CRASHES** |
| Crash with empty Sequence `[]` | **YES** - not file-size related |

## Workaround

Convert shard to pandas and recreate the Dataset to break internal Arrow references:

```python
shard = ds.shard(num_shards=num_shards, index=i, contiguous=True)

# CRITICAL: Pandas round-trip breaks problematic references
shard_df = shard.to_pandas()
fresh_shard = Dataset.from_pandas(shard_df, preserve_index=False)
fresh_shard = fresh_shard.cast(ds.features)

# Now embedding works
table = fresh_shard._data.table.combine_chunks()
embedded = embed_table_storage(table) # OK!
```

## Disproven Hypotheses

| Hypothesis | Test | Result |
|------------|------|--------|
| PyArrow 2GB binary limit | Monkey-patched `Nifti.pa_type` to `pa.large_binary()` | Still crashed |
| Memory fragmentation | Called `table.combine_chunks()` | Still crashed |
| File size issue | Tested with tiny NIfTI files | Still crashed |

## Root Cause Hypothesis

When `ds.shard()` or `ds.select()` creates a subset, the resulting Arrow table retains internal references/views to the parent table. When `embed_table_storage` processes nested struct types like `Sequence(Nifti())`, these references cause a crash in the C++ layer.

The pandas round-trip forces a full data copy, breaking these problematic references.

## Environment

- datasets version: main branch (post-0.22.0)
- Platform: macOS 14.x ARM64 (may be platform-specific)
- Python: 3.13
- PyArrow: 18.1.0

## Notes

This may ultimately be a PyArrow issue surfacing through datasets. Happy to help debug further if maintainers can point to where to look in the embedding logic.