datasets cannot handle nested json if features is given.
Hi ! `Sequence` has a weird behavior for dictionaries (from tensorflow-datasets), use a regular list instead:

```python
ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
    'ref1': datasets.Value('string'),
    'ref2': datasets.Value('string'),
    'cuts': [{
        "cut1": datasets.Value("uint16"),
        "cut2": datasets.Value("uint16")
    }]
}))
``` > Hi ! `Sequence` has a weird behavior for dictionaries (from tensorflow-datasets), use a regular list instead:
> 
> ```python
> ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
>     'ref1': datasets.Value('string'),
>     'ref2': datasets.Value('string'),
>     'cuts': [{
>         "cut1": datasets.Value("uint16"),
>         "cut2": datasets.Value("uint16")
>     }]
> }))
> ```
Thank you!
 It works.
### Describe the bug

I have a json named temp.json.
```json
{"ref1": "ABC", "ref2": "DEF", "cuts":[{"cut1": 3, "cut2": 5}]}
```
I want to load it.
```python
ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
    'ref1': datasets.Value('string'),
    'ref2': datasets.Value('string'),
    'cuts': datasets.Sequence({
        "cut1": datasets.Value("uint16"),
        "cut2": datasets.Value("uint16")
    })
}))
```
The above code does not work. However, I can load it without giving features.
```python
ds = datasets.load_dataset('json', data_files="./temp.json")
```
Is it possible to load integers as uint16 to save some memory?

### Steps to reproduce the bug

As in the bug description.

### Expected behavior

The data are loaded and integers are uint16.

### Environment info

Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 2.21.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0