## Bug description

Monthly Active Users metric seems wrong when choosing "# Number" as chart type.

Number: 
![image](https://user-images.githubusercontent.com/513669/201980874-be7d9c0d-5c9c-4c1e-955c-ea4143c84061.png)

Line chart: 
![image](https://user-images.githubusercontent.com/513669/201980986-b17af929-84e5-4f6a-b7f8-23b7ac8bedbc.png)

**490 != 70**

## How to reproduce

1. Create a new insight using any event and the "Monthly active users" metric
2. Date range = This month + Grouped by = Day
3. Choose chart type as Number => remember value
4. Change chart type as Linear => check last data point

=> the 2 values do not match
=> in other words there's a diff between a total aggregation and an aggregation per bucket of time ?

## Environment

- [x] PostHog Cloud
posthog/queries/breakdown_props.py
elif breakdown_type == "group":
        value_expression, _ = get_property_string_expr(
            table="events" if direct_on_events else "groups",
            property_name=cast(str, breakdown),
            var="%(key)s",
            column=f"group{breakdown_group_type_index}_properties"
            if direct_on_events
            else f"group_properties_{breakdown_group_type_index}",
            materialised_table_column=f"group{breakdown_group_type_index}_properties"
            if direct_on_events
            else "group_properties",
        )
    else:
        value_expression = get_single_or_multi_property_string_expr(
            breakdown, table="events", query_alias=None, column="properties", normalize_url=breakdown_normalize_url
        )
    if cast_as_float:
        value_expression = f"toFloat64OrNull(toString({value_expression}))"
    return f"{value_expression} AS value"
def _to_bucketing_expression(bin_count: int) -> str:
    if bin_count <= 1:
        qunatile_expression = "quantiles(0,1)(value)"
    else:
        quantiles = []
        bin_size = 1.0 / bin_count
        for i in range(bin_count + 1):
            quantiles.append(i * bin_size)
        qunatile_expression = f"quantiles({','.join([f'{quantile:.2f}' for quantile in quantiles])})(value)"
    return f"arrayCompact(arrayMap(x -> floor(x, 2), {qunatile_expression}))"
def _format_all_query(team: Team, filter: Filter, **kwargs) -> Tuple[str, Dict]: