"Couldn't cast array of type" in complex datasets
### Describe the bug

When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value.

This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level.

Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided.

### Steps to reproduce the bug

A trivial reproduction case:

```python
from typing import Iterator, Any
import pandas as pd
from datasets import Dataset

def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]:
    for i in range(next(iter(lengths))):
        yield {feature: values[i] for feature, values in batch.items()}

def examples_to_batch(examples) -> dict[str, list[Any]]:
    batch = {}

    for example in examples:
        for feature, value in example.items():
            if feature not in batch:
                batch[feature] = []
            batch[feature].append(value)

    return batch

def batch_process(examples, explicit_schema: bool):
    new_examples = []
    for example in batch_to_examples(examples):
        new_examples.append(dict(texts=example["raw_text"].split()))
    return examples_to_batch(new_examples)

df = pd.DataFrame(
    [
        {"raw_text": ""},
        {"raw_text": "This is a test"},
        {"raw_text": "This is another test"},
    ]
)

dataset = Dataset.from_pandas(df)

# datasets won't be able to typehint a dataset that starts with an empty example.
with pytest.raises(TypeError, match="Couldn't cast array of type"):
    dataset = dataset.map(
        batch_process,
        batched=True,
        batch_size=1,
        num_proc=1,
        remove_columns=dataset.column_names,
    )
```

This results in crashes like:

```bash
  File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper
    return func(array, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper
    return func(array, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast
    raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type string to null
```

### Expected behavior

The code should successfully map and create a new dataset without error.

### Environment info

Mac OSX, Linux
Thanks for reporting! 

Specifying the target features explicitly should avoid this error:
```python
dataset = dataset.map(
    batch_process,
    batched=True,
    batch_size=1,
    num_proc=1,
    remove_columns=dataset.column_names,
    features=datasets.Features({"texts": datasets.Sequence(datasets.Value("string"))})
)
```

This error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution. Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?

Feels like something that would be easy to implement and could save memory / deal with this case in a standardized way. > . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?

Interesting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails. @mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here.