## Bug description

When calculating Trends results with an aggregated (e.g. single output, like big number) display type, WAU/MAU don't work. This is because we have an `ACTIVE_USERS_SQL` query for time series display types, but we don't have a version of this for aggregated display types. (See ZEN-1043.)

## How to reproduce

1. Create an insight with a single WAU or MAU series.
2. Switch display type to "Number".
3. Open the "Debug ClickHouse queries" modal from the command palette.
4. See `COUNT(*)` instead of `COUNT(DISTINCT person_id)`.

#### *Thank you* for your bug report – we love squashing them!

posthog/queries/trends/breakdown.py
inner_sql = VOLUME_PER_ACTOR_BREAKDOWN_INNER_SQL.format(
                    breakdown_filter=breakdown_filter,
                    person_join=person_join_condition,
                    groups_join=groups_join_condition,
                    sessions_join=sessions_join_condition,
                    aggregate_operation=aggregate_operation,
                    interval_annotation=interval_annotation,
                    aggregator=self.actor_aggregator,
                    breakdown_value=breakdown_value,
                    start_of_week_fix=start_of_week_fix(self.filter.interval),
                    **breakdown_filter_params,
                )
            else:
                inner_sql = BREAKDOWN_INNER_SQL.format(
                    breakdown_filter=breakdown_filter,
                    person_join=person_join_condition,
                    groups_join=groups_join_condition,
                    sessions_join=sessions_join_condition,
                    aggregate_operation=aggregate_operation,
                    interval_annotation=interval_annotation,
                    breakdown_value=breakdown_value,
                    start_of_week_fix=start_of_week_fix(self.filter.interval),
                    **breakdown_filter_params,
                )
            breakdown_query = BREAKDOWN_QUERY_SQL.format(
                interval=interval_annotation, num_intervals=num_intervals, inner_sql=inner_sql
            )
            self.params.update({"seconds_in_interval": seconds_in_interval, "num_intervals": num_intervals})
            return breakdown_query, self.params, self._parse_trend_result(self.filter, self.entity)
    def _breakdown_cohort_params(self):