ENH: sparse_series_to_coo performance
#### Is your feature request related to a problem?

Converting a sparse Series to a `scipy.sparse.coo_matrix` could be much faster. I think the `get_indexer` function defined in [`_to_ijv`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/sparse/scipy_sparse.py#L22) adds unnecessary complexity. 

#### Describe the solution you'd like

It can be much faster by accessing the `codes` attribute of the multiindex, as follows:

```
i_coord, j_coord = ss.index.codes
i_labels, j_labels = ss.index.levels
```

for a two-level multiindex. It should be straightforward to extend to more levels I think.

#### API breaking implications

None

#### Describe alternatives you've considered

None

#### Additional context

To give an example, I started digging into this problem because I had a 2-level-MultiIndexed Series with 61M rows, that is to be converted to a 1M x 1500 sparse matrix. Making the conversion using `to_coo()` took 10min, making it as described above took half a second.