YAML integer keys are not preserved Hub server-side

After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
  - See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
    - Original:
      ```yaml
      class_label:
        names:
          0: B-long
          1: B-short
      ```
    - Returned by the server:
      ```yaml
      class_label:
        names:
          '0': B-long
          '1': B-short
      ```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
  ```yaml
  "id2label": {
    "0": "CONTRADICTION",
    "1": "NEUTRAL",
    "2": "ENTAILMENT"
  }
  ``` 

On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.

Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])                                                                                      
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)

~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
   1743                 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
   1744 
-> 1745         return cls.from_dict(from_yaml_inner(yaml_data))
   1746 
   1747     def encode_example(self, example):

~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
   1739             elif isinstance(obj, list):
   1740                 names = [_feature.pop("name") for _feature in obj]
-> 1741                 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
   1742             else:
   1743                 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")

~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
   1739             elif isinstance(obj, list):
   1740                 names = [_feature.pop("name") for _feature in obj]
-> 1741                 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
   1742             else:
   1743                 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")

~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
   1734                             return {"_type": snakecase_to_camelcase(obj["dtype"])}
   1735                     else:
-> 1736                         return from_yaml_inner(obj["dtype"])
   1737                 else:
   1738                     return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}

~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
   1736                         return from_yaml_inner(obj["dtype"])
   1737                 else:
-> 1738                     return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
   1739             elif isinstance(obj, list):
   1740                 names = [_feature.pop("name") for _feature in obj]

~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
   1704             if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
   1705                 label_ids = sorted(feature["class_label"]["names"])
-> 1706                 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
   1707                     raise ValueError(
   1708                         f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."

TypeError: can only concatenate str (not "int") to str
```

TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets