Adding new `index_name` argument to Dataframe Collections

[john-sanchez31]

Add a new optional argument, index_name, to DataFrame Collections.
When this argument is provided, the DataFrame index is materialized as a new column with the given name.

Behavior:

• If index_name is set, a new column is created in the DataFrame collection containing the index values.
• The original DataFrame index remains unchanged.
• The new column may participate in uniqueness constraints if its values are unique.

Validations:

• Type Validation: Valid types are str, range (default) and numbers (integers or floats)
• Name conflict: The name of the column must not conflict with the columns in the Dataframe
• Uniqueness: Uniqueness of the index (if custom indexes are allowed, this validation may be required)

Note: This new columns can be added to the list of unique_column_names.
This is determinate by the uniqueness of the column

Exmple:

df = pd.DataFrame({
  "A": [1, 2, 3] * 2,
  "B": ["A", "B] * 3,
}, index=range(6))

pydough.dataframe_collection(
        name="my_df",
        dataframe=df,
        unique_column_names=["C", ["A", "B"]],
        index_name="C"
    )

The Dataframe collection would be created with a new column called "C" that contains [0, 1, 2, 3, 4, 5]
Result

     C      A      B
     0      1      A
     1      2      B
     2      3      A 
     3      1      B
     4      2      A
     5      3      B

Unique columns validation: Include the ability to have at least one column from unique_column_names in filter_columns instead of all being required.

Example:
unique columns are ["column1", ["column2", "column3"]]
but in the filter column I can include column1 only or column2 and column3 without column1

This also requires smarter validation for unique_columns. Making sure that if a unique column is compose of more than one column, all of them are included in the filter_column if provided. Following the last example, if column1 is not included it must include column2 AND column3.