Dataset.from_pandas preserves useless index
Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. 
## Describe the bug
Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:

```
import pandas as pd
from datasets import Dataset


df = pd.read_csv('some_dataset.csv')
# Some DataFrame preprocessing code...
dataset = Dataset.from_pandas(df)
```
If your preprocessing code contain indexing operations like this:
```
df = df[df.col1 == some_value]
```
then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,
                9,
            ...
            83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,
            83988],
           dtype='int64', length=16590)```

In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.

You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.

If you approve that this isn't desirable behavior, I can make a PR fixing that.

## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1

