The global ordinals terms aggregator has an option to remap global ordinals to
dense ordinal that match the request. This mode is automatically picked when the terms
aggregator is a child of another bucket aggregator or when it needs to defer buckets to an
aggregation that is used in the ordering of the terms.
Though when building the final buckets, this aggregator loops over all possible global ordinals
rather than using the hash map that was built to remap the ordinals.
For fields with high cardinality this is highly inefficient and can lead to slow responses even
when the number of terms that match the query is low.
This change fixes this performance issue by using the hash table of matching ordinals to perform
the pruning of the final buckets for the terms and significant_terms aggregation.
I ran a simple benchmark with 1M documents containing 0 to 10 keywords randomly selected among 1M unique terms.
This field is used to perform a multi-level terms aggregation using rally to collect the response times.
The aggregation below is an example of a two-level terms aggregation that was used to perform the benchmark:

```
"aggregations":{
   "1":{
      "terms":{
         "field":"keyword"
      },
      "aggregations":{
         "2":{
            "terms":{
               "field":"keyword"
            }
         }
      }
   }
}
```

| Levels of aggregation | 50th percentile ms (master) | 50th percentile ms (patch) |
| --- | --- | --- |
| 2 | 640.41ms | 577.499ms |
| 3 | 2239.66ms | 600.154ms |
| 4 | 14141.2ms | 703.512ms |

Closes elastic#30117