Preserve JSON column order and support list of strings field

Preserve column order when loading from a JSON file with a list of dict (or with a field containing a list of dicts).

Additionally, support JSON file with a list of strings field.

Fix #6913.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6914). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005492 / 0.011353 (-0.005861) | 0.004087 / 0.011008 (-0.006921) | 0.065334 / 0.038508 (0.026826) | 0.032282 / 0.023109 (0.009173) | 0.246441 / 0.275898 (-0.029457) | 0.278807 / 0.323480 (-0.044673) | 0.003245 / 0.007986 (-0.004741) | 0.003795 / 0.004328 (-0.000534) | 0.050082 / 0.004250 (0.045832) | 0.050613 / 0.037052 (0.013561) | 0.258885 / 0.258489 (0.000396) | 0.297257 / 0.293841 (0.003416) | 0.028847 / 0.128546 (-0.099699) | 0.011377 / 0.075646 (-0.064270) | 0.206089 / 0.419271 (-0.213182) | 0.037354 / 0.043533 (-0.006178) | 0.257319 / 0.255139 (0.002180) | 0.275134 / 0.283200 (-0.008066) | 0.018064 / 0.141683 (-0.123619) | 1.112371 / 1.452155 (-0.339783) | 1.160909 / 1.492716 (-0.331807) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.101893 / 0.018006 (0.083887) | 0.311084 / 0.000490 (0.310594) | 0.000208 / 0.000200 (0.000008) | 0.000042 / 0.000054 (-0.000013) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019548 / 0.037411 (-0.017863) | 0.064396 / 0.014526 (0.049870) | 0.074900 / 0.176557 (-0.101656) | 0.122750 / 0.737135 (-0.614385) | 0.076693 / 0.296338 (-0.219646) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.288609 / 0.215209 (0.073400) | 2.831354 / 2.077655 (0.753699) | 1.453961 / 1.504120 (-0.050159) | 1.327702 / 1.541195 (-0.213493) | 1.382140 / 1.468490 (-0.086351) | 0.568465 / 4.584777 (-4.016312) | 2.427199 / 3.745712 (-1.318513) | 2.810586 / 5.269862 (-2.459275) | 1.839227 / 4.565676 (-2.726449) | 0.063219 / 0.424275 (-0.361056) | 0.005111 / 0.007607 (-0.002496) | 0.341447 / 0.226044 (0.115403) | 3.357429 / 2.268929 (1.088501) | 1.806501 / 55.444624 (-53.638123) | 1.541696 / 6.876477 (-5.334781) | 1.755400 / 2.142072 (-0.386673) | 0.661442 / 4.805227 (-4.143785) | 0.120203 / 6.500664 (-6.380461) | 0.044429 / 0.075469 (-0.031040) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.987810 / 1.841788 (-0.853978) | 12.765467 / 8.074308 (4.691159) | 10.497788 / 10.191392 (0.306396) | 0.132723 / 0.680424 (-0.547701) | 0.014484 / 0.534201 (-0.519717) | 0.285763 / 0.579283 (-0.293520) | 0.264377 / 0.434364 (-0.169987) | 0.326971 / 0.540337 (-0.213367) | 0.429432 / 1.386936 (-0.957504) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005996 / 0.011353 (-0.005357) | 0.004092 / 0.011008 (-0.006916) | 0.051660 / 0.038508 (0.013152) | 0.036661 / 0.023109 (0.013552) | 0.271133 / 0.275898 (-0.004765) | 0.295728 / 0.323480 (-0.027752) | 0.004452 / 0.007986 (-0.003534) | 0.002915 / 0.004328 (-0.001413) | 0.050669 / 0.004250 (0.046418) | 0.044431 / 0.037052 (0.007378) | 0.284683 / 0.258489 (0.026194) | 0.318799 / 0.293841 (0.024958) | 0.031094 / 0.128546 (-0.097452) | 0.010810 / 0.075646 (-0.064836) | 0.059740 / 0.419271 (-0.359531) | 0.034912 / 0.043533 (-0.008621) | 0.268779 / 0.255139 (0.013640) | 0.291294 / 0.283200 (0.008095) | 0.019769 / 0.141683 (-0.121914) | 1.124833 / 1.452155 (-0.327322) | 1.168301 / 1.492716 (-0.324416) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.097080 / 0.018006 (0.079074) | 0.304636 / 0.000490 (0.304146) | 0.000232 / 0.000200 (0.000032) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023186 / 0.037411 (-0.014225) | 0.082232 / 0.014526 (0.067706) | 0.089427 / 0.176557 (-0.087130) | 0.132715 / 0.737135 (-0.604421) | 0.092820 / 0.296338 (-0.203518) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.300672 / 0.215209 (0.085463) | 2.969603 / 2.077655 (0.891948) | 1.577827 / 1.504120 (0.073707) | 1.440768 / 1.541195 (-0.100427) | 1.494526 / 1.468490 (0.026035) | 0.574599 / 4.584777 (-4.010178) | 0.963300 / 3.745712 (-2.782412) | 2.847854 / 5.269862 (-2.422008) | 1.841248 / 4.565676 (-2.724428) | 0.062321 / 0.424275 (-0.361954) | 0.005389 / 0.007607 (-0.002218) | 0.350853 / 0.226044 (0.124808) | 3.463514 / 2.268929 (1.194586) | 1.937661 / 55.444624 (-53.506964) | 1.665320 / 6.876477 (-5.211157) | 1.849028 / 2.142072 (-0.293044) | 0.655333 / 4.805227 (-4.149894) | 0.119062 / 6.500664 (-6.381602) | 0.043387 / 0.075469 (-0.032082) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.004118 / 1.841788 (-0.837670) | 13.350894 / 8.074308 (5.276585) | 11.179363 / 10.191392 (0.987971) | 0.135169 / 0.680424 (-0.545255) | 0.016298 / 0.534201 (-0.517903) | 0.288467 / 0.579283 (-0.290816) | 0.132712 / 0.434364 (-0.301651) | 0.325436 / 0.540337 (-0.214901) | 0.413406 / 1.386936 (-0.973530) |

</details>
</details>

![](https://cml.dev/watermark.png#670e1cf31606f397ae0f858b568b1b4ed50c1843 "CML watermark")