Use MultiIndex for ExtraP Functions

Nobody has claimed this yet.

The `componentize_statsframe` and `_add_extrap_statistics` extrap model functions both add a lot of columns to the statsframe.

**Statsframe**
| avg#inclusive#sum#time.duration_extrap-model | avg#inclusive#sum#time.duration_RSS_extrap-model | avg#inclusive#sum#time.duration_rRSS_extrap-model | ... |
| ------ | ------ | ------ | ------|
| cell | cell | cell | cell |
| cell | cell | cell | cell |

Currently, we are appending strings to the column name to distinguish them between each other. This becomes very messy fast and is not user friendly. I propose pulling out the common strings between the columns, like `avg#inclusive#sum#time.duration`, into a higher-level column index, and let the different strings distinguish each column, like `model`, `RSS`, `rRSS`.

And what if our statsframe already had other statistics and/or models in it? Our problem would only become worse. So I believe we can leverage multi-indexing to help us organize the statsframe.

Start by reading the componentize_statsframe and _add_extrap_statistics functions to understand how they currently construct statsframe column names. The work is done when extrapolated statistics use organized multi-level columns that remain usable alongside existing statistics and models, rather than appending all distinctions into strings.

Written by the indexing model from the issue text.