Avoid saving sparse ChunkedArrays in pyarrow tables

Fixes https://github.com/huggingface/datasets/issues/5541


_The documentation is not available anymore as the PR was closed or merged._

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

PyArrow==6.0.0

<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) | 0.008452 / 0.011353 (-0.002901) | 0.004500 / 0.011008 (-0.006508) | 0.100103 / 0.038508 (0.061595) | 0.029395 / 0.023109 (0.006286) | 0.297740 / 0.275898 (0.021842) | 0.359132 / 0.323480 (0.035652) | 0.007045 / 0.007986 (-0.000941) | 0.003415 / 0.004328 (-0.000913) | 0.076389 / 0.004250 (0.072138) | 0.036612 / 0.037052 (-0.000440) | 0.308773 / 0.258489 (0.050284) | 0.345701 / 0.293841 (0.051860) | 0.033230 / 0.128546 (-0.095317) | 0.011463 / 0.075646 (-0.064183) | 0.322382 / 0.419271 (-0.096890) | 0.041194 / 0.043533 (-0.002339) | 0.300685 / 0.255139 (0.045546) | 0.323076 / 0.283200 (0.039876) | 0.087330 / 0.141683 (-0.054353) | 1.508661 / 1.452155 (0.056506) | 1.531776 / 1.492716 (0.039059) |

### 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) | 0.188391 / 0.018006 (0.170385) | 0.400102 / 0.000490 (0.399612) | 0.002006 / 0.000200 (0.001806) | 0.000075 / 0.000054 (0.000021) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023232 / 0.037411 (-0.014179) | 0.097313 / 0.014526 (0.082787) | 0.106244 / 0.176557 (-0.070313) | 0.141180 / 0.737135 (-0.595955) | 0.107871 / 0.296338 (-0.188468) |

### 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) | 0.418610 / 0.215209 (0.203400) | 4.162243 / 2.077655 (2.084588) | 1.884300 / 1.504120 (0.380180) | 1.694197 / 1.541195 (0.153002) | 1.727740 / 1.468490 (0.259250) | 0.692129 / 4.584777 (-3.892648) | 3.364230 / 3.745712 (-0.381482) | 1.871507 / 5.269862 (-3.398355) | 1.261520 / 4.565676 (-3.304156) | 0.083258 / 0.424275 (-0.341017) | 0.012479 / 0.007607 (0.004872) | 0.528802 / 0.226044 (0.302757) | 5.281029 / 2.268929 (3.012100) | 2.402222 / 55.444624 (-53.042403) | 2.064954 / 6.876477 (-4.811522) | 2.027044 / 2.142072 (-0.115029) | 0.813124 / 4.805227 (-3.992103) | 0.149397 / 6.500664 (-6.351267) | 0.065032 / 0.075469 (-0.010437) |

### 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) | 1.239192 / 1.841788 (-0.602595) | 13.529913 / 8.074308 (5.455605) | 14.253251 / 10.191392 (4.061859) | 0.165145 / 0.680424 (-0.515278) | 0.028367 / 0.534201 (-0.505834) | 0.395121 / 0.579283 (-0.184162) | 0.405372 / 0.434364 (-0.028992) | 0.472201 / 0.540337 (-0.068137) | 0.560620 / 1.386936 (-0.826316) |

</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) | 0.006368 / 0.011353 (-0.004985) | 0.004542 / 0.011008 (-0.006466) | 0.076361 / 0.038508 (0.037853) | 0.026893 / 0.023109 (0.003784) | 0.341210 / 0.275898 (0.065312) | 0.378377 / 0.323480 (0.054898) | 0.004833 / 0.007986 (-0.003153) | 0.003358 / 0.004328 (-0.000970) | 0.075516 / 0.004250 (0.071265) | 0.038841 / 0.037052 (0.001788) | 0.342230 / 0.258489 (0.083741) | 0.384317 / 0.293841 (0.090476) | 0.031874 / 0.128546 (-0.096672) | 0.011651 / 0.075646 (-0.063995) | 0.085816 / 0.419271 (-0.333455) | 0.042389 / 0.043533 (-0.001144) | 0.340678 / 0.255139 (0.085539) | 0.367441 / 0.283200 (0.084241) | 0.089748 / 0.141683 (-0.051935) | 1.487358 / 1.452155 (0.035203) | 1.615049 / 1.492716 (0.122333) |

### 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) | 0.220933 / 0.018006 (0.202926) | 0.397162 / 0.000490 (0.396673) | 0.002336 / 0.000200 (0.002136) | 0.000069 / 0.000054 (0.000015) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025004 / 0.037411 (-0.012407) | 0.100877 / 0.014526 (0.086351) | 0.110624 / 0.176557 (-0.065932) | 0.152042 / 0.737135 (-0.585094) | 0.112951 / 0.296338 (-0.183388) |

### 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) | 0.441071 / 0.215209 (0.225862) | 4.419471 / 2.077655 (2.341817) | 2.082976 / 1.504120 (0.578856) | 1.884023 / 1.541195 (0.342828) | 1.950590 / 1.468490 (0.482100) | 0.706104 / 4.584777 (-3.878673) | 3.329825 / 3.745712 (-0.415887) | 1.868850 / 5.269862 (-3.401011) | 1.178785 / 4.565676 (-3.386892) | 0.083910 / 0.424275 (-0.340365) | 0.012296 / 0.007607 (0.004689) | 0.542998 / 0.226044 (0.316953) | 5.429944 / 2.268929 (3.161015) | 2.502285 / 55.444624 (-52.942339) | 2.150507 / 6.876477 (-4.725970) | 2.170492 / 2.142072 (0.028420) | 0.813410 / 4.805227 (-3.991817) | 0.152310 / 6.500664 (-6.348354) | 0.066999 / 0.075469 (-0.008470) |

### 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) | 1.290839 / 1.841788 (-0.550949) | 14.089491 / 8.074308 (6.015183) | 13.704922 / 10.191392 (3.513530) | 0.130089 / 0.680424 (-0.550335) | 0.017000 / 0.534201 (-0.517201) | 0.381173 / 0.579283 (-0.198110) | 0.389271 / 0.434364 (-0.045093) | 0.461700 / 0.540337 (-0.078637) | 0.556428 / 1.386936 (-0.830508) |

</details>
</details>

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