When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way

## Describe the bug
Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on.

It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me.

## Steps to reproduce the bug
I don't have explicit code to repro the bug, but ill show an example

Code prior to the fix:
```python
def preprocess(examples):
    # returns an encoded data dict with keys that match the features, but the types do not match
...

def get_encoded_data(data):
  dataset = Dataset.from_pandas(data)
      unique_labels = data['audit_type'].unique().tolist()
      features = Features({
          'image': Array3D(dtype="uint8", shape=(3, 224, 224))),
          'input_ids': Sequence(feature=Value(dtype='int64'))),
          'attention_mask': Sequence(Value(dtype='int64'))),
          'token_type_ids': Sequence(Value(dtype='int64'))),
          'bbox': Array2D(dtype="int64", shape=(512, 4))),
          'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
      })

      encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names)
```

The Features set that fixed it:
```python
    features = Features({
        'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))),
        'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))),
        'attention_mask': Sequence(Sequence(Value(dtype='int64'))),
        'token_type_ids': Sequence(Sequence(Value(dtype='int64'))),
        'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))),
        'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
    })
```
The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not.

## Expected results
Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated.

## Actual results
Specify the actual results or traceback.
Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious

Example errors:
```
OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB.
(offset overflow while concatenating arrays)
```

```
zsh: killed     python doc_classification.py

UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
datasets version: 2.1.0
Platform: macOS-12.2.1-arm64-arm-64bit
Python version: 3.9.12
PyArrow version: 6.0.1
Pandas version: 1.4.2


Comments:
Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match.

It would be awesome if we had a way to reproduce the `OverflowError` in this case, to better understand what happened and be able to provide the best error message