load_dataset with multiple jsonlines files interprets datastructure too early

### Describe the bug

likely related to #6460 

using `datasets.load_dataset("json", data_dir= ... )` with multiple `.jsonl` files will error if one of the files (maybe the first file?) contains a full column of empty data. 

### Steps to reproduce the bug

real world example:
data is available in this [PR-branch](https://github.com/Vipitis/shadertoys-dataset/pull/3/commits/cb1e7157814f74acb09d5dc2f1be3c0a868a9933). Because my files are chunked by months, some months contain all empty data for some columns, just by chance - these are `[]`. Otherwise it's all the same structure. 

```python
from datasets import load_dataset
ds = load_dataset("json", data_dir="./data/annotated/api")
```

you get a long error trace, where in the middle it says something like 
```cs
TypeError: Couldn't cast array of type struct<id: int64, src: string, ctype: string, channel: int64, sampler: struct<filter: string, wrap: string, vflip: string, srgb: string, internal: string>, published: int64> to null
```

toy example: (on request)


### Expected behavior

Some suggestions 
1. give a better error message to the user
2. consider all files before deciding on a data structure for a given column.
3. if you encounter a new structure, and can't cast that to null, replace the null-hypothesis. (maybe something for pyarrow)

as a workaround I have lazily implemented the following (essentially step 2)
```python
import os 
import jsonlines
import datasets

api_files = os.listdir("./data/annotated/api")

api_files = [f"./data/annotated/api/{f}" for f in api_files]

api_file_contents = []
for f in api_files:
    with jsonlines.open(f) as reader:
        for obj in reader:
            api_file_contents.append(obj)

ds = datasets.Dataset.from_list(api_file_contents)
```
this works fine for my usecase, but is potentially slower and less memory efficient for really large datasets (where this is unlikely to happen in the first place).

### Environment info

- `datasets` version: 2.20.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0

I’ll take a look

Possible definitions of done for this issue:

1. A fix so you can load your dataset specifically
2. A general fix for datasets similar to this in the `datasets` library

Option 1 is trivial. I think option 2 requires significant changes to the library.

Since you outlined something akin to option 2 in `Expected behavior` I'm assuming that's what you'd like to see done. Is that right?

In the meantime, here's a solution for option 1:

```python
import datasets

data_dir = './data/annotated/api'

features = datasets.Features({'id': datasets.Value(dtype='string'),
 'name': datasets.Value(dtype='string'),
 'author': datasets.Value(dtype='string'),
 'description': datasets.Value(dtype='string'),
 'tags': datasets.Sequence(feature=datasets.Value(dtype='string'), length=-1),
 'likes': datasets.Value(dtype='int64'),
 'viewed': datasets.Value(dtype='int64'),
 'published': datasets.Value(dtype='int64'),
 'date': datasets.Value(dtype='string'),
 'time_retrieved': datasets.Value(dtype='string'),
 'image_code': datasets.Value(dtype='string'),
 'image_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'common_code': datasets.Value(dtype='string'),
 'sound_code': datasets.Value(dtype='string'),
 'sound_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'buffer_a_code': datasets.Value(dtype='string'),
 'buffer_a_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'buffer_b_code': datasets.Value(dtype='string'),
 'buffer_b_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'buffer_c_code': datasets.Value(dtype='string'),
 'buffer_c_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'buffer_d_code': datasets.Value(dtype='string'),
 'buffer_d_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'cube_a_code': datasets.Value(dtype='string'),
 'cube_a_inputs': [{'channel': datasets.Value(dtype='int64'),
   'ctype': datasets.Value(dtype='string'),
   'id': datasets.Value(dtype='int64'),
   'published': datasets.Value(dtype='int64'),
   'sampler': {'filter': datasets.Value(dtype='string'),
    'internal': datasets.Value(dtype='string'),
    'srgb': datasets.Value(dtype='string'),
    'vflip': datasets.Value(dtype='string'),
    'wrap': datasets.Value(dtype='string')},
   'src': datasets.Value(dtype='string')}],
 'thumbnail': datasets.Value(dtype='string'),
 'access': datasets.Value(dtype='string'),
 'license': datasets.Value(dtype='string'),
 'functions': datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value(dtype='int64'), length=-1), length=-1),
 'test': datasets.Value(dtype='string')})

datasets.load_dataset('json', data_dir=data_dir, features=features)
```

As pointed out by @hvaara, you can define explicit features so that you avoid the `datasets` library having to infer them (from the first few samples).

Note that the feature inference is done from the first few samples of JSON-Lines on purpose, so that the entire data does not need to be parsed twice (it would be inefficient for very large datasets).

I understand this. But can there be a solution that doesn't require the end user to write this shema by hand(in my case there is some fields that contain a nested structure)? 

Maybe offer an option to infer the shema automatically before loading the dataset. Or perhaps - trigger such a method when this error arises? 

Is this "first few files" heuristics accessible via kwargs perhaps. Maybe an error that says 
`Cloud not cast some structure into feature shema, consider increasing shema_files to a large number or all".

There might be efficient implementations to solve this problem for larger datasets. 

@Vipitis raised a good point on the HF Discord regarding the use of a [dataset script](https://huggingface.co/docs/datasets/en/dataset_script) to provide the schema during initialization. Using this approach requires setting `trust_remote_code=True`, which is not allowed in certain evaluation frameworks.

For cases where using a dataset script is acceptable, would it be helpful to add functionality to the library (not necessarily in `load_dataset`) that can automatically discover the feature definitions and output them, so you don't have to manually define them?

Alternatively, for situations where features need to be known at load-time without using a dataset script, another option could be loading the dataset schema from a file format that doesn't require `trust_remote_code=True`.