Epochs shortened after resuming mid-epoch with Iterable dataset+StatefulDataloader(persistent_workers=True)

### Describe the bug

When `torchdata.stateful_dataloader.StatefulDataloader(persistent_workers=True)` the epochs after resuming only iterate through the examples that were left in the epoch when the training was interrupted. For example, in the script below training is interrupted on step 124 (epoch 1) when 3 batches are left. Then after resuming, the rest of epochs (2 and 3) only iterate through these 3 batches.

### Steps to reproduce the bug

Run the following script with and with PERSISTENT_WORKERS=true.

```python
# !/usr/bin/env python3
# torch==2.5.1
# datasets==3.3.2
# torchdata>=0.9.0
import datasets
import pprint
from torchdata.stateful_dataloader import StatefulDataLoader

import os

PERSISTENT_WORKERS = (
    os.environ.get("PERSISTENT_WORKERS", "False").lower() == "true"
)

# PERSISTENT_WORKERS = True  # Incorrect resume


# ds = datasets.load_from_disk("dataset").to_iterable_dataset(num_shards=4)
def generator():
    for i in range(128):
        yield {"x": i}


ds = datasets.Dataset.from_generator(
    generator, features=datasets.Features({"x": datasets.Value("int32")})
).to_iterable_dataset(num_shards=4)

dl = StatefulDataLoader(
    ds, batch_size=2, num_workers=2, persistent_workers=PERSISTENT_WORKERS
)
global_step = 0
epoch = 0
ds_state_dict = None
state_dict = None
resumed = False
while True:
    if epoch >= 3:
        break
    if state_dict is not None:
        dl.load_state_dict(state_dict)
        state_dict = None
        ds_state_dict = None
        resumed = True
        print("resumed")
    for i, batch in enumerate(dl):
        print(f"epoch: {epoch}, global_step: {global_step}, batch: {batch}")
        global_step += 1  # consume datapoint
        # simulate error
        if global_step == 124 and not resumed:
            ds_state_dict = ds.state_dict()
            state_dict = dl.state_dict()
            print("checkpoint")
            print("ds_state_dict")
            pprint.pprint(ds_state_dict)
            print("dl_state_dict")
            pprint.pprint(state_dict)
            break

    if state_dict is None:
        ds.set_epoch(epoch)
        epoch += 1
```

The script checkpoints when there are three batches left in the second epoch. After resuming, only the last three batches are repeated in the rest of the epochs.

If it helps, following are the two state_dicts for the dataloader save at the same step with the two settings. The left one is for `PERSISTENT_WORKERS=False`

![Image](https://github.com/user-attachments/assets/c97d6502-d7bd-4ef4-ae2d-66fe1a9732b1)

### Expected behavior

All the elements in the dataset should be iterated through in the epochs following the one where we resumed. The expected behavior can be seen by setting `PERSISTENT_WORKERS=False`. 

### Environment info

torch==2.5.1
datasets==3.3.2
torchdata>=0.9.0

Thanks for reporting ! Maybe we should store the epoch in the state_dict, and then when the dataset is iterated on again after setting a new epoch it should restart from scratch instead of resuming ? wdyt ?

But why does this only happen when `persistent_workers=True`? I would expect it to work correctly even without storing the epoch number in the state_dict of the iterable dataset. 

I think persistent_workers=False simply ignores the dataset state_dict when it starts a new epoch, that's why the issue doesn't appear in that case

I opened https://github.com/huggingface/datasets/pull/7451 to fix the issue, let me know if it works for you

I just released `datasets` 3.4 that includes the fix :)

PS: in your script you probably want to set the epoch like this, otherwise it's still set to 0 after the first epoch:

```diff
    if state_dict is None:
-       ds.set_epoch(epoch)
        epoch += 1
+       ds.set_epoch(epoch)
```

@lhoestq 
If I understand correctly, the issue was:
when training saves a checkpoint of dataloader in epoch 1, the resumed training only consumes partial data in epoch 2, 3, etc.

However, with the fix we are facing the issue that:
when training saves a checkpoint of dataloader in epoch 2, the resumed training starts from scratch instead of consuming remaining partial data in epoch 2.

This makes training inconsistent between resuming from a checkpoint vs. original training if continued without a checkpoint.