## 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)`.

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posthog/queries/trends/sql.py
)
"""
BREAKDOWN_QUERY_SQL = """
SELECT groupArray(day_start) as date, groupArray(count) as data, breakdown_value FROM (
    SELECT SUM(total) as count, day_start, breakdown_value FROM (
        SELECT * FROM (
            -- Create a table with 1 row for each interval for the requested date range
            -- This acts as a method of zero filling, i.e. when there are no data points
            -- for a given interval, we'll still have a row for the group by interval with
            -- a 0 value.
            --
            -- It's essentially a cross product of graph "ticks" and breakdown values.
            --
            -- TODO: we're relying on num_intervals, seconds_int_interval etc. being passed
            --       in as a parameter. To reduce the coupling between here and the
            --       calling code, we could perform calculations for these within the query
            --       itself based on date_to/date_from. We could also pass in the intervals
            --       explicitly, although we'll be relying on the date handling between python
            --       and ClickHouse to be the same.
            --
            -- NOTE: there is the ORDER BY ... WITH FILL Expression but I'm not sure how we'd
            --       handle the edge cases:
            --
            --          https://clickhouse.com/docs/en/sql-reference/statements/select/order-by/#orderby-with-fill
            --
            SELECT
                toUInt16(0) AS total,
                ticks.day_start as day_start,
                breakdown_value
            FROM (