Sample without replacement option when interleaving datasets
@lhoestq Continuing on the idea from https://github.com/huggingface/datasets/issues/217 
This doesn't add a new stopping criteria, but a new argument to interleave_datasets method. Let me know what you think and if you see a better way of doing this I'm open to suggestions. Great ! this is a cool additions :)

IMO sample_with_replacement as a new argument doesn't make sense if the strategy is "first_exhausted", which is the default, and since disabling replacement affects the stopping strategy, I would be in favor of having it as a new strategy instead Makes sense, here's a revised implementation with that argument removed and adding a new stopping strategy. The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7786). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. @lhoestq Let me know if there's anything on my side that I can do! Hi @radulescupetru, I'm commenting here after @lhoestq mentioned this PR in #7792. I'm facing a similar problem and I was wondering if there was a common a solution. Let me know if we share the same problem.

As described in the issue, my problem is that I want to mix unbalanced datasets, distribute the samples on multiple workers and ranks, without repeating samples and while retrieving most samples as I can (i.e. without discarding samples whenever they could actually be used). I also noticed that the current approaches `interleave_dataset` or `concatenate_dataset` do not leverage all the workers if the number of shards do not align with the number of workers. I pushed a small update @radulescupetru related to @LTMeyer 's issue, I hope you don't mind.

The logic looks all good to me now :) could you also update `_interleave_map_style_datasets()` in `arrow_dataset.py` before we merge ? This way the `Dataset` objects will also benefit from this new stopping strategy. @lhoestq Thanks for that fix. I've pushed updates to support the new stopping strategy for map style datasets as well.
Right now, `interleave_datasets` function with probabilities will sample with replacement. The PR adds the ability to sample without replacement.

```
import datasets

# Create datasets of different sizes to test exhaustion
data_a = [{"value": i, "source": "A"} for i in range(5)]
data_b = [{"value": i, "source": "B"} for i in range(10, 15)]

ds_a = datasets.Dataset.from_list(data_a).to_iterable_dataset()
ds_b = datasets.Dataset.from_list(data_b).to_iterable_dataset()

# Interleave with probabilities
ds_interleaved = datasets.interleave_datasets(
    [ds_a, ds_b],
    probabilities=[0.6, 0.4],
    seed=42,
    stopping_strategy="all_exhausted",
    sample_with_replacement=True,
)
for i, example in enumerate(ds_interleaved):
    print(f"Sample:{i}: value:{example['value']:02d} source:{example['source']}")
```
In this example, `sample_with_replacement=True` and it prints:
```
Sample:0: value:10 source:B
Sample:1: value:00 source:A
Sample:2: value:11 source:B
Sample:3: value:12 source:B
Sample:4: value:01 source:A
Sample:5: value:13 source:B
Sample:6: value:14 source:B
Sample:7: value:10 source:B
Sample:8: value:02 source:A
Sample:9: value:03 source:A
Sample:10: value:04 source:A
```
Note that sample with value:10 source: B is sampled twice (Sample:0 and Sample:7)

Re-running with `sample_with_replacement=False` in prints:
```
Sample:0: value:10 source:B
Sample:1: value:00 source:A
Sample:2: value:11 source:B
Sample:3: value:12 source:B
Sample:4: value:01 source:A
Sample:5: value:13 source:B
Sample:6: value:14 source:B
Sample:7: value:02 source:A
Sample:8: value:03 source:A
Sample:9: value:04 source:A
```
Note that we don't see any repeated items.