Parquet size grows exponential for categorical data
### Describe the bug

It seems that when saving a data frame with a categorical column inside the size can grow exponentially.

This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data.


### Steps to reproduce the bug

To reproduce the bug, it is enough to run this script:

```
import pandas as pd
import os

if __name__ == "__main__":
    for n in [10, 1e2, 1e3, 1e4, 1e5]:
        for n_col in [1, 10, 100, 1000, 10000]:
            input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))])
            input.iloc[0:100].to_parquet("a.parquet")
            for col in input.columns:
                input[col] = input[col].astype("category")
            input.iloc[0:100].to_parquet("b.parquet")
            a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024)
            b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024)
            print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}")
```

That produces this output:

<img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15">



### Expected behavior

In my opinion either:

1. The two file should have (almost) the same size
2. There should be warning telling the user that such difference in size is possible

### Environment info

Python 3.8.18
pandas==2.0.3
numpy==1.24.4