### Description

Replace the existing selects with multi-selects that allow you to filter multiple DAG run states and types at the same time, somewhat similar to my prototype:

![image](https://github.com/apache/airflow/assets/1842905/c4ec0793-1ccb-417d-989c-781997416f97)


### Use case/motivation

I'm not sure if it is just me, but sometimes I wish I was able to show multiple DAG run states, especially `running` and `failed`, at the same time. 
This would be especially helpful for busy DAGs on which I want to clear a few failed runs. Without the multi-select, if I switch from `failed` to `running` DAG runs, I need to orient myself again to find the run I just cleared (assuming there are lots of other running DAG runs). _With_ the multi-select, the DAG run I just cleared stays in the same spot and I can check the logs, while clearing some other failed runs.

I'm not sure we need a multi-select for DAG run types as well. I'd tend to say no, but maybe someone else has a use-case for that.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

TaskFail.dag_id == dag.dag_id,
                DagRun.execution_date >= min_date,
                DagRun.execution_date <= base_date,
            )
        )
        if dag.partial:
            ti_fails = ti_fails.where(TaskFail.task_id.in_([t.task_id for t in dag.tasks]))
        ti_fails = session.scalars(ti_fails)
        fails_totals: dict[tuple[str, str, str], int] = defaultdict(int)
        for failed_task_instance in ti_fails:
            dict_key = (
                failed_task_instance.dag_id,
                failed_task_instance.task_id,
                failed_task_instance.run_id,
            )
            if failed_task_instance.duration:
                fails_totals[dict_key] += failed_task_instance.duration
        
        def grouping_key(ti: TaskInstance):
            return ti.dag_id, ti.task_id, ti.run_id
        mapped_tis = set()
        for _, group in itertools.groupby(sorted(task_instances, key=grouping_key), key=grouping_key):
            tis = list(group)
            duration = sum(x.duration for x in tis if x.duration)
            if duration:
                first_ti = tis[0]
                if first_ti.map_index >= 0:
                    mapped_tis.add(first_ti.task_id)
                date_time = wwwutils.epoch(first_ti.execution_date)
                x_points[first_ti.task_id].append(date_time)