ENH: Drop Index Combinations from Multiindex Dataframe
#### Is your feature request related to a problem?

I whish the [pandas drop function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) would let me drop combination of rows (or columns) in multiindex dataframes.
E.g., I have a multiindex dataframe like this

|         |         | Column 1 | Column 2 |
| ------- | ------- | -------- | -------- |
| Index 1 | Index 2 |          |          |
| A       | a       | 1        | 2        |
|         | b       | 3        | 4        |
| B       | a       | 5        | 6        |
|         | b       | 7        | 8        |

And I want to drop only the Index combination ``(B, a)``.
With the current implementation of the drop function, this is not possible.


#### Describe the solution you'd like

Maybe adding an additional parameter called ``combination``, which is a boolean value, could solve the problem.
When setting ``combination=True``, the list-like ``label`` parameter could be recognized as the combination to drop, which would then result in
```python
df = df.drop(labels=["B", "a"], axis=0, combination=True)
df
```
|         |         | Column 1 | Column 2 |
| ------- | ------- | -------- | -------- |
| Index 1 | Index 2 |          |          |
| A       | a       | 1        | 2        |
|         | b       | 3        | 4        |
| B       | b       | 7        | 8        |

#### API breaking implications

I think adding the parameter would not break the API.

#### Describe alternatives you've considered

I have not considered alternatives

#### Additional context

For my problem, I implemented a function similar to the one below

```python
def drop(df: pd.DataFrame, index1: Optional[str]=None, index2: Optional[int]=None) -> pd.DataFrame:
    if index1 is None:
        if index2 is None:
            # if no index1 and index2 is defined, return the original dataframe
            return df
        else:
            return df.drop(index2, level="Index2").sort_index()
    else:
        if index2 is None:
            return df.drop(index1, level="Index1").sort_index()
        else:
            a = df.drop(index1, level="Index1")
            b = pd.concat([df.loc[index1].drop(index2, level="Index2")], keys=[index1], names=["Index1"])
            return pd.concat([a, b]).sort_index()

```
