### 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)

],
                    "label": "group",
                    "tooltip": "",
                },
            ],
            "id": None,
            "instances": [],
            "label": None,
        },
        "ordering": ["data_interval_end", "execution_date"],
    }
def test_query_count(dag_with_runs, session):
    run1, run2 = dag_with_runs
    with assert_queries_count(2):
        dag_to_grid(run1.dag, (run1, run2), session)
def test_has_outlet_dataset_flag(admin_client, dag_maker, session, app, monkeypatch):
    with monkeypatch.context() as m:
        
        m.setattr("airflow.plugins_manager.global_operator_extra_links", [])
        m.setattr("airflow.plugins_manager.operator_extra_links", [])
        m.setattr("airflow.plugins_manager.registered_operator_link_classes", {})
        with dag_maker(dag_id=DAG_ID, serialized=True, session=session):
            lineagefile = File("/tmp/does_not_exist")
            EmptyOperator(task_id="task1")
            EmptyOperator(task_id="task2", outlets=[lineagefile])
            EmptyOperator(task_id="task3", outlets=[Dataset("foo"), lineagefile])
            EmptyOperator(task_id="task4", outlets=[Dataset("foo")])
        m.setattr(app, "dag_bag", dag_maker.dagbag)
        resp = admin_client.get(f"/object/grid_data?dag_id={DAG_ID}", follow_redirects=True)
    def _expected_task_details(task_id, has_outlet_datasets):