Fix parquet export infos

Don't use the parquet export infos when USE_PARQUET_EXPORT is False.

Otherwise the `datasets-server` might reuse erroneous data when re-running a job

this follows https://github.com/huggingface/datasets/pull/6714

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6822). 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) | 0.005084 / 0.011353 (-0.006269) | 0.003658 / 0.011008 (-0.007351) | 0.063369 / 0.038508 (0.024860) | 0.030739 / 0.023109 (0.007630) | 0.244335 / 0.275898 (-0.031564) | 0.271731 / 0.323480 (-0.051749) | 0.004133 / 0.007986 (-0.003853) | 0.002798 / 0.004328 (-0.001530) | 0.048790 / 0.004250 (0.044540) | 0.044054 / 0.037052 (0.007002) | 0.261514 / 0.258489 (0.003025) | 0.292155 / 0.293841 (-0.001686) | 0.027971 / 0.128546 (-0.100575) | 0.010723 / 0.075646 (-0.064923) | 0.207328 / 0.419271 (-0.211944) | 0.035928 / 0.043533 (-0.007605) | 0.245320 / 0.255139 (-0.009819) | 0.268774 / 0.283200 (-0.014426) | 0.017119 / 0.141683 (-0.124564) | 1.107052 / 1.452155 (-0.345103) | 1.151752 / 1.492716 (-0.340965) |

### 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) | 0.089941 / 0.018006 (0.071935) | 0.299788 / 0.000490 (0.299298) | 0.000211 / 0.000200 (0.000012) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018159 / 0.037411 (-0.019252) | 0.061876 / 0.014526 (0.047350) | 0.074733 / 0.176557 (-0.101824) | 0.122070 / 0.737135 (-0.615065) | 0.076100 / 0.296338 (-0.220238) |

### 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) | 0.282209 / 0.215209 (0.067000) | 2.758098 / 2.077655 (0.680444) | 1.482454 / 1.504120 (-0.021666) | 1.372649 / 1.541195 (-0.168546) | 1.373171 / 1.468490 (-0.095319) | 0.563606 / 4.584777 (-4.021171) | 2.406760 / 3.745712 (-1.338952) | 2.796322 / 5.269862 (-2.473540) | 1.732327 / 4.565676 (-2.833350) | 0.063623 / 0.424275 (-0.360652) | 0.005338 / 0.007607 (-0.002269) | 0.337562 / 0.226044 (0.111518) | 3.345225 / 2.268929 (1.076296) | 1.844353 / 55.444624 (-53.600271) | 1.551003 / 6.876477 (-5.325474) | 1.570623 / 2.142072 (-0.571449) | 0.644843 / 4.805227 (-4.160385) | 0.118811 / 6.500664 (-6.381853) | 0.041731 / 0.075469 (-0.033738) |

### 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) | 0.970469 / 1.841788 (-0.871319) | 11.775531 / 8.074308 (3.701222) | 9.757852 / 10.191392 (-0.433540) | 0.130187 / 0.680424 (-0.550237) | 0.013654 / 0.534201 (-0.520547) | 0.328387 / 0.579283 (-0.250896) | 0.268181 / 0.434364 (-0.166183) | 0.325230 / 0.540337 (-0.215107) | 0.421055 / 1.386936 (-0.965881) |

</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) | 0.005846 / 0.011353 (-0.005507) | 0.003606 / 0.011008 (-0.007402) | 0.050787 / 0.038508 (0.012279) | 0.031635 / 0.023109 (0.008526) | 0.277040 / 0.275898 (0.001142) | 0.300544 / 0.323480 (-0.022936) | 0.004200 / 0.007986 (-0.003786) | 0.002749 / 0.004328 (-0.001580) | 0.049449 / 0.004250 (0.045198) | 0.041616 / 0.037052 (0.004564) | 0.289570 / 0.258489 (0.031081) | 0.316138 / 0.293841 (0.022297) | 0.029578 / 0.128546 (-0.098969) | 0.010582 / 0.075646 (-0.065064) | 0.058284 / 0.419271 (-0.360988) | 0.033078 / 0.043533 (-0.010455) | 0.277964 / 0.255139 (0.022825) | 0.295008 / 0.283200 (0.011808) | 0.017753 / 0.141683 (-0.123930) | 1.128635 / 1.452155 (-0.323519) | 1.190142 / 1.492716 (-0.302575) |

### 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) | 0.091504 / 0.018006 (0.073498) | 0.303875 / 0.000490 (0.303385) | 0.000221 / 0.000200 (0.000021) | 0.000052 / 0.000054 (-0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021413 / 0.037411 (-0.015998) | 0.074825 / 0.014526 (0.060299) | 0.086329 / 0.176557 (-0.090228) | 0.125632 / 0.737135 (-0.611503) | 0.087918 / 0.296338 (-0.208420) |

### 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) | 0.297914 / 0.215209 (0.082705) | 2.922885 / 2.077655 (0.845230) | 1.625758 / 1.504120 (0.121638) | 1.500174 / 1.541195 (-0.041021) | 1.517162 / 1.468490 (0.048672) | 0.576885 / 4.584777 (-4.007892) | 2.458723 / 3.745712 (-1.286989) | 2.798471 / 5.269862 (-2.471391) | 1.762499 / 4.565676 (-2.803178) | 0.064736 / 0.424275 (-0.359539) | 0.005325 / 0.007607 (-0.002282) | 0.351697 / 0.226044 (0.125652) | 3.496223 / 2.268929 (1.227294) | 1.977535 / 55.444624 (-53.467090) | 1.695223 / 6.876477 (-5.181254) | 1.689692 / 2.142072 (-0.452381) | 0.656404 / 4.805227 (-4.148823) | 0.123106 / 6.500664 (-6.377558) | 0.040980 / 0.075469 (-0.034489) |

### 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) | 1.036972 / 1.841788 (-0.804816) | 12.163931 / 8.074308 (4.089623) | 10.297927 / 10.191392 (0.106535) | 0.144087 / 0.680424 (-0.536337) | 0.015553 / 0.534201 (-0.518648) | 0.286225 / 0.579283 (-0.293058) | 0.275567 / 0.434364 (-0.158797) | 0.332717 / 0.540337 (-0.207620) | 0.423804 / 1.386936 (-0.963132) |

</details>
</details>

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