[Python][Parquet] Improve usability of ParquetDataset

In e.g. pd.read_parquet implementation, we currently wrap pq.read_table. However, I would like to use the dataset implementation to get access to some more features (e.g. reading only the first few rows, defining a filter after opening (discovering the schema) the dataset).

That runs into some issues:

• pq.read_table() uses ParquetDataset under the hood, but still has a fallback to plain ParquetFile().read() when pyarrow.dataset module is not available. While this will definitely be uncommon, I have no clue how important it is to keep supporting this (also on the pandas side, since this is currently a "feature" of pandas.read_parquet by means of using pq.read_table).
• I would prefer using pq.ParquetDataset over pyarrow.dataset, because the parquet version is compatible with pq.read_table and does all the translation to the pyarrow.dataset API for us (constructing the file format object, passing the various keywords in the correct place, mapping some naming differences, etc)
• But ParquetDataset is also limited, and currently I am essentially using it as a constructor to then access the underlying ._dataset (the pyarrow.dataset dataset object)

Some ideas that I was having:

• Add a new pq.open_dataset()-like function that also has the boilerplate to construct the pyarrow.dataset object, but returns that instead of the ParquetDataset wrapper
• Would it be technically possible to let pq.ParquetDataset inherit from pyarrow.dataset.Dataset, so you get those methods that way, while keeping it back-compat
• Add new methods to pq.ParquetDataset (to_table(), head(), etc) to make it look more like a pyarrow.dataset.Dataset, but without actually inheriting from it
• Simply "officially" expose the underlying dataset, so that I don't have to use the private _dataset attribute

Bắt đầu bằng việc xem xét sự tích hợp của pandas.read_parquet và pq.read_table, sau đó kiểm tra pq.ParquetDataset và _dataset bên dưới của nó cùng với API pyarrow.dataset. So sánh hàm open_dataset được đề xuất, tính kế thừa, các phương thức được thêm vào và việc công khai dataset; hoàn thành có nghĩa là lựa chọn và ghi lại một hướng khả dụng tương thích với phạm vi API phù hợp.