Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs
### Describe the bug

I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used.

I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down.

### Steps to reproduce the bug

```python
import datasets

dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)])

def test_func(row, idx):
    if idx==58:
        return {'output': []}
    else:
        return {'output' : [{'test':1}, {'test':2}]}

# this works fine
test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4)

# this fails
test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32)
>ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null").
```

The error occurs during the check

```python
_check_if_features_can_be_aligned([dset.features for dset in dsets])
```

When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error.

### Expected behavior

Expected behavior is the result would be the same regardless of the `num_proc` value used.

### Environment info

Datasets version 2.11.0
Python 3.9.16
This scenario currently requires explicitly passing the target features (to avoid the error): 
```python
import datasets

...

features = dataset.features
features["output"] = = [{"test": datasets.Value("int64")}]
test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)
```