Add section in tutorial for IterableDataset

Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in:

- #5410 

_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) | 0.008492 / 0.011353 (-0.002861) | 0.004717 / 0.011008 (-0.006292) | 0.101111 / 0.038508 (0.062602) | 0.029129 / 0.023109 (0.006019) | 0.307564 / 0.275898 (0.031666) | 0.367038 / 0.323480 (0.043558) | 0.007105 / 0.007986 (-0.000881) | 0.003622 / 0.004328 (-0.000706) | 0.078370 / 0.004250 (0.074120) | 0.036960 / 0.037052 (-0.000093) | 0.315612 / 0.258489 (0.057123) | 0.353601 / 0.293841 (0.059760) | 0.032900 / 0.128546 (-0.095647) | 0.011405 / 0.075646 (-0.064241) | 0.322331 / 0.419271 (-0.096940) | 0.040823 / 0.043533 (-0.002710) | 0.306734 / 0.255139 (0.051595) | 0.328155 / 0.283200 (0.044955) | 0.087169 / 0.141683 (-0.054514) | 1.460543 / 1.452155 (0.008389) | 1.498094 / 1.492716 (0.005378) |

### 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) | 0.011863 / 0.018006 (-0.006143) | 0.416315 / 0.000490 (0.415826) | 0.003463 / 0.000200 (0.003263) | 0.000075 / 0.000054 (0.000020) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023219 / 0.037411 (-0.014192) | 0.096469 / 0.014526 (0.081943) | 0.105960 / 0.176557 (-0.070596) | 0.148993 / 0.737135 (-0.588142) | 0.108112 / 0.296338 (-0.188226) |

### 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) | 0.415662 / 0.215209 (0.200453) | 4.155111 / 2.077655 (2.077456) | 1.834943 / 1.504120 (0.330823) | 1.622752 / 1.541195 (0.081557) | 1.701630 / 1.468490 (0.233140) | 0.690596 / 4.584777 (-3.894181) | 3.399385 / 3.745712 (-0.346327) | 3.140521 / 5.269862 (-2.129341) | 1.609152 / 4.565676 (-2.956524) | 0.082132 / 0.424275 (-0.342143) | 0.012343 / 0.007607 (0.004735) | 0.532715 / 0.226044 (0.306670) | 5.323032 / 2.268929 (3.054104) | 2.326625 / 55.444624 (-53.118000) | 1.944263 / 6.876477 (-4.932213) | 1.994015 / 2.142072 (-0.148058) | 0.813805 / 4.805227 (-3.991422) | 0.149233 / 6.500664 (-6.351431) | 0.065318 / 0.075469 (-0.010151) |

### 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) | 1.212441 / 1.841788 (-0.629347) | 13.979069 / 8.074308 (5.904761) | 14.003998 / 10.191392 (3.812606) | 0.146956 / 0.680424 (-0.533468) | 0.028564 / 0.534201 (-0.505637) | 0.392370 / 0.579283 (-0.186913) | 0.399695 / 0.434364 (-0.034669) | 0.473481 / 0.540337 (-0.066856) | 0.562625 / 1.386936 (-0.824311) |

</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) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076217 / 0.038508 (0.037709) | 0.028888 / 0.023109 (0.005779) | 0.345431 / 0.275898 (0.069533) | 0.389246 / 0.323480 (0.065766) | 0.005939 / 0.007986 (-0.002046) | 0.003356 / 0.004328 (-0.000973) | 0.075880 / 0.004250 (0.071629) | 0.041427 / 0.037052 (0.004374) | 0.344481 / 0.258489 (0.085992) | 0.398508 / 0.293841 (0.104667) | 0.031801 / 0.128546 (-0.096745) | 0.011763 / 0.075646 (-0.063884) | 0.085600 / 0.419271 (-0.333672) | 0.042656 / 0.043533 (-0.000876) | 0.345893 / 0.255139 (0.090754) | 0.376910 / 0.283200 (0.093711) | 0.092451 / 0.141683 (-0.049232) | 1.461222 / 1.452155 (0.009068) | 1.555822 / 1.492716 (0.063106) |

### 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) | 0.235781 / 0.018006 (0.217774) | 0.418485 / 0.000490 (0.417995) | 0.005560 / 0.000200 (0.005360) | 0.000078 / 0.000054 (0.000023) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025410 / 0.037411 (-0.012001) | 0.103780 / 0.014526 (0.089254) | 0.110183 / 0.176557 (-0.066374) | 0.151097 / 0.737135 (-0.586039) | 0.112539 / 0.296338 (-0.183799) |

### 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) | 0.436686 / 0.215209 (0.221477) | 4.341594 / 2.077655 (2.263940) | 2.062309 / 1.504120 (0.558190) | 1.857461 / 1.541195 (0.316267) | 1.947204 / 1.468490 (0.478713) | 0.699641 / 4.584777 (-3.885136) | 3.406983 / 3.745712 (-0.338729) | 3.294705 / 5.269862 (-1.975157) | 1.360582 / 4.565676 (-3.205095) | 0.083025 / 0.424275 (-0.341250) | 0.012461 / 0.007607 (0.004854) | 0.537767 / 0.226044 (0.311722) | 5.393316 / 2.268929 (3.124387) | 2.516692 / 55.444624 (-52.927932) | 2.163987 / 6.876477 (-4.712490) | 2.220480 / 2.142072 (0.078408) | 0.810648 / 4.805227 (-3.994579) | 0.151820 / 6.500664 (-6.348844) | 0.068080 / 0.075469 (-0.007389) |

### 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) | 1.279382 / 1.841788 (-0.562405) | 13.989947 / 8.074308 (5.915638) | 14.039229 / 10.191392 (3.847836) | 0.141071 / 0.680424 (-0.539352) | 0.017118 / 0.534201 (-0.517083) | 0.381558 / 0.579283 (-0.197725) | 0.390407 / 0.434364 (-0.043957) | 0.440920 / 0.540337 (-0.099418) | 0.525478 / 1.386936 (-0.861458) |

</details>
</details>

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