Fix streaming pandas.read_excel

This PR fixes `xpandas_read_excel`:
- Support passing a path string, besides a file-like object
- Support passing `use_auth_token`
- First assumes the host server supports HTTP range requests; only if a ValueError is thrown (Cannot seek streaming HTTP file), then it preserves previous behavior (see [#3355](https://github.com/huggingface/datasets/pull/3355)).

Fix https://huggingface.co/datasets/bigbio/meqsum/discussions/1
Fix:
- https://github.com/bigscience-workshop/biomedical/issues/801

Related to:
- #3355

_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) | 0.009517 / 0.011353 (-0.001835) | 0.005210 / 0.011008 (-0.005798) | 0.098916 / 0.038508 (0.060408) | 0.036123 / 0.023109 (0.013014) | 0.301564 / 0.275898 (0.025666) | 0.358086 / 0.323480 (0.034606) | 0.008159 / 0.007986 (0.000174) | 0.004122 / 0.004328 (-0.000206) | 0.075899 / 0.004250 (0.071648) | 0.046082 / 0.037052 (0.009030) | 0.302871 / 0.258489 (0.044382) | 0.351162 / 0.293841 (0.057321) | 0.038215 / 0.128546 (-0.090331) | 0.012026 / 0.075646 (-0.063620) | 0.330988 / 0.419271 (-0.088284) | 0.048351 / 0.043533 (0.004818) | 0.291840 / 0.255139 (0.036701) | 0.320387 / 0.283200 (0.037187) | 0.105018 / 0.141683 (-0.036665) | 1.447158 / 1.452155 (-0.004997) | 1.491205 / 1.492716 (-0.001511) |

### 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) | 0.250870 / 0.018006 (0.232863) | 0.562974 / 0.000490 (0.562484) | 0.001789 / 0.000200 (0.001589) | 0.000252 / 0.000054 (0.000197) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028208 / 0.037411 (-0.009203) | 0.110897 / 0.014526 (0.096371) | 0.120394 / 0.176557 (-0.056163) | 0.164980 / 0.737135 (-0.572156) | 0.126283 / 0.296338 (-0.170056) |

### 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) | 0.397922 / 0.215209 (0.182713) | 3.969233 / 2.077655 (1.891578) | 1.766422 / 1.504120 (0.262302) | 1.577503 / 1.541195 (0.036308) | 1.672344 / 1.468490 (0.203854) | 0.695708 / 4.584777 (-3.889069) | 3.770763 / 3.745712 (0.025051) | 3.369592 / 5.269862 (-1.900269) | 1.851122 / 4.565676 (-2.714554) | 0.084063 / 0.424275 (-0.340212) | 0.012156 / 0.007607 (0.004549) | 0.534639 / 0.226044 (0.308594) | 5.021955 / 2.268929 (2.753027) | 2.215438 / 55.444624 (-53.229186) | 1.890459 / 6.876477 (-4.986018) | 2.071361 / 2.142072 (-0.070712) | 0.834623 / 4.805227 (-3.970604) | 0.165588 / 6.500664 (-6.335076) | 0.064336 / 0.075469 (-0.011133) |

### 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) | 1.205651 / 1.841788 (-0.636136) | 14.916871 / 8.074308 (6.842563) | 14.559495 / 10.191392 (4.368103) | 0.166889 / 0.680424 (-0.513535) | 0.028645 / 0.534201 (-0.505556) | 0.433634 / 0.579283 (-0.145649) | 0.429849 / 0.434364 (-0.004515) | 0.508617 / 0.540337 (-0.031720) | 0.595261 / 1.386936 (-0.791675) |

</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) | 0.007696 / 0.011353 (-0.003657) | 0.005434 / 0.011008 (-0.005574) | 0.099234 / 0.038508 (0.060725) | 0.033904 / 0.023109 (0.010795) | 0.379181 / 0.275898 (0.103283) | 0.401858 / 0.323480 (0.078379) | 0.006257 / 0.007986 (-0.001729) | 0.004406 / 0.004328 (0.000077) | 0.073174 / 0.004250 (0.068923) | 0.056033 / 0.037052 (0.018981) | 0.379375 / 0.258489 (0.120886) | 0.425928 / 0.293841 (0.132087) | 0.037476 / 0.128546 (-0.091071) | 0.012520 / 0.075646 (-0.063127) | 0.364975 / 0.419271 (-0.054297) | 0.049341 / 0.043533 (0.005808) | 0.370519 / 0.255139 (0.115380) | 0.390585 / 0.283200 (0.107385) | 0.113339 / 0.141683 (-0.028344) | 1.460575 / 1.452155 (0.008421) | 1.564951 / 1.492716 (0.072235) |

### 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) | 0.246217 / 0.018006 (0.228210) | 0.554358 / 0.000490 (0.553869) | 0.000451 / 0.000200 (0.000251) | 0.000059 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029557 / 0.037411 (-0.007855) | 0.110472 / 0.014526 (0.095946) | 0.122652 / 0.176557 (-0.053904) | 0.159396 / 0.737135 (-0.577739) | 0.128852 / 0.296338 (-0.167486) |

### 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) | 0.447927 / 0.215209 (0.232718) | 4.448292 / 2.077655 (2.370637) | 2.228874 / 1.504120 (0.724754) | 2.030231 / 1.541195 (0.489036) | 2.116417 / 1.468490 (0.647927) | 0.702713 / 4.584777 (-3.882064) | 3.774063 / 3.745712 (0.028351) | 3.521662 / 5.269862 (-1.748200) | 1.476700 / 4.565676 (-3.088976) | 0.084921 / 0.424275 (-0.339354) | 0.012862 / 0.007607 (0.005255) | 0.559142 / 0.226044 (0.333098) | 5.512233 / 2.268929 (3.243305) | 2.750024 / 55.444624 (-52.694600) | 2.388845 / 6.876477 (-4.487632) | 2.541786 / 2.142072 (0.399714) | 0.842256 / 4.805227 (-3.962971) | 0.168088 / 6.500664 (-6.332576) | 0.064211 / 0.075469 (-0.011258) |

### 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) | 1.239001 / 1.841788 (-0.602787) | 15.286345 / 8.074308 (7.212036) | 13.883981 / 10.191392 (3.692589) | 0.186212 / 0.680424 (-0.494212) | 0.018305 / 0.534201 (-0.515896) | 0.420459 / 0.579283 (-0.158824) | 0.421039 / 0.434364 (-0.013325) | 0.487348 / 0.540337 (-0.052989) | 0.587730 / 1.386936 (-0.799206) |

</details>
</details>

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