Improve performance of JSON loader

As reported by @natolambert, loading regular JSON files with `datasets` shows poor performance.

The cause is that we use the `json` Python standard library instead of other faster libraries. See my old comment: https://github.com/huggingface/datasets/pull/2638#pullrequestreview-706983714
> There are benchmarks that compare different JSON packages, with the Standard Library one among the worst performant: 
> - https://github.com/ultrajson/ultrajson#benchmarks
> - https://github.com/ijl/orjson#performance

I remember having a discussion about this and it was decided that it was better not to include an additional dependency on a 3rd-party library.

However:
- We already depend on `pandas` and `pandas` depends on `ujson`: so we have an indirect dependency on `ujson`
- Even if the above were not the case, we always could include `ujson` as an optional extra dependency, and check at runtime if it is installed to decide which library to use, either json or ujson

Thanks! Feel free to ping me for examples. May not respond immediately because we're all busy but would like to help.

Hi @natolambert, could you please give some examples of JSON files to benchmark?

Please note that this JSON file (https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/eval-set-scores/Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback.json) is not in "records" orient; instead it has the following structure:
```json
{
  "chat_template": "tulu",
  "id": [30, 34, 35,...],
  "model": "Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback",
  "model_type": "Seq. Classifier",
  "results": [1, 1, 1, ...],
  "scores_chosen": [4.421875, 1.8916015625, 3.8515625,...],
  "scores_rejected": [-2.416015625, -1.47265625, -0.9912109375,...],
  "subset": ["alpacaeval-easy", "alpacaeval-easy", "alpacaeval-easy",...]
  "text_chosen": ["

 How do I detail a...",...],
  "text_rejected": ["

 How do I detail a...",...]
}
```

Note that "records" orient should be a list (not a dict) with each row as one item of the list:
```json
[
  {"chat_template": "tulu", "id": 30,... },
  {"chat_template": "tulu", "id": 34,... },
  ...
]
```

We use a mix (which is a mess), here's an example with the records orient
https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/best-of-n/alpaca_eval/tulu-13b/OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5.json

There are more in that folder, ~40mb maybe?

@albertvillanova here's a snippet so you don't need to click
```
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        0
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 3.076171875
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        1
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 3.87890625
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        2
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 3.287109375
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        3
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 1.6337890625
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        4
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 5.27734375
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        5
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 3.0625
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        6
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 2.29296875
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        7
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 6.77734375
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        8
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 3.853515625
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        9
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 4.86328125
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        10
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 2.890625
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        11
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 4.70703125
}
{
    "config": "top_p=0.9;temp=1.0",
    "dataset_details": "helpful_base",
    "id": [
        0,
        12
    ],
    "model": "allenai/tulu-2-dpo-13b",
    "scores": 4.45703125
}
```

Thanks again for your feedback, @natolambert.

However, strictly speaking, the last file is not in JSON format but in kind of JSON-Lines like format (although not properly either because there are multiple newline characters within each object). Not even pandas can read that file format.

Anyway, for JSON-Lines, I would expect that `datasets` and `pandas` have the same performance for JSON Lines files, as both use `pyarrow` under the hood...

A proper JSON file in records orient should be a list (a JSON array): the first character should be `[`.

Anyway, I am generating a JSON file from your JSON-Lines file to test performance.