Do not require extra copy of dimensional goals aggregated data when evaluating without dimensions

E.g. when evaluating aggregated data of [`test-mutli-dimension`](https://github.com/avast/ep-stats/blob/e58d003235504f86e5599746f28e81fbfe6663b3/src/epstats/toolkit/testing/resources/goals_agg.csv#L156) experiment in [`test_multi_dimension`](https://github.com/avast/ep-stats/blob/e58d003235504f86e5599746f28e81fbfe6663b3/tests/epstats/toolkit/test_experiment.py#L385), we require to have another copy of aggregated data without dimensional columns see [here](https://github.com/avast/ep-stats/blob/e58d003235504f86e5599746f28e81fbfe6663b3/src/epstats/toolkit/testing/resources/goals_agg.csv#L160).

It would be nice just to "group by" dimensional data without the need to have extra aggregated data without dimensions in agg goals dataframe.

current data:
```
test-multi-dimension a test_unit_type global exposure 1000 1000 1000 1000 1000
test-multi-dimension b test_unit_type global exposure 1001 1001 1001 1001 1001
test-multi-dimension a test_unit_type unit view button-1 p-1 200 200 200 200 200
test-multi-dimension b test_unit_type unit view button-1 p-1 220 220 220 220 220
test-multi-dimension a test_unit_type unit view button-1 100 100 100 100 100
test-multi-dimension b test_unit_type unit view button-1 180 180 180 180 180
test-multi-dimension a test_unit_type unit view 300 300 300 300 300
test-multi-dimension b test_unit_type unit view 400 400 400 400 400
```

data format requested in this issue:
```
test-multi-dimension a test_unit_type global exposure 1000 1000 1000 1000 1000
test-multi-dimension b test_unit_type global exposure 1001 1001 1001 1001 1001
test-multi-dimension a test_unit_type unit view button-1 p-1 200 200 200 200 200
test-multi-dimension b test_unit_type unit view button-1 p-1 220 220 220 220 220
test-multi-dimension a test_unit_type unit view button-1 100 100 100 100 100
test-multi-dimension b test_unit_type unit view button-1 180 180 180 180 180
```

Start with tests/epstats/toolkit/test_experiment.py at test_multi_dimension and inspect the referenced goals_agg.csv data around the linked rows. Trace how aggregated goals are evaluated without dimensions. Done means the dimensional rows can be grouped and evaluated without requiring a separate copy of the aggregated data with dimensional columns removed.

Written by the indexing model from the issue text.