### Description

Allow users to re-use recent DagRun configurations upon running a DAG.
This can be achieved by adding a dropdown that contains some information about recent configurations. When user selects an item, the relevant JSON configuration can be pasted to the "Configuration JSON" textbox.
<img width="692" alt="Screen Shot 2022-11-16 at 16 22 30" src="https://user-images.githubusercontent.com/39705397/202209536-c709ec75-c768-48ab-97d4-82b02af60569.png">
<img width="627" alt="Screen Shot 2022-11-16 at 16 22 38" src="https://user-images.githubusercontent.com/39705397/202209553-08828521-dba2-4e83-8e2a-6dec850086de.png">
<img width="612" alt="Screen Shot 2022-11-16 at 16 38 40" src="https://user-images.githubusercontent.com/39705397/202209755-0946521a-e1a5-44cb-ae74-d43ca3735f31.png">


### Use case/motivation

Commonly, DAGs are triggered using repetitive configurations. Sometimes the same configuration is used for triggering a DAG, and sometimes, the configuration differs by just a few parameters. 

This interaction forces a user to store the templates he uses somewhere on his machine or to start searching for the configuration he needs in `dagrun/list/`, which does take extra time.

It will be handy to offer a user an option to select one of the recent configurations upon running a DAG.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] 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)

dag = dag.partial_subset(task_ids_or_regex=root, include_downstream=False, include_upstream=True)
        dag_states = (
            session.query(
                (_convert_to_date(session, DagRun.execution_date)).label("date"),
                DagRun.state,
                func.max(DagRun.data_interval_start).label("data_interval_start"),
                func.max(DagRun.data_interval_end).label("data_interval_end"),
                func.count("*").label("count"),
            )
            .filter(DagRun.dag_id == dag.dag_id)
            .group_by(_convert_to_date(session, DagRun.execution_date), DagRun.state)
            .order_by(_convert_to_date(session, DagRun.execution_date).asc())
            .all()
        )
        data_dag_states = [
            {
                
                
                "date": dr.date if isinstance(dr.date, str) else dr.date.isoformat(),
                "state": dr.state,
                "count": dr.count,
            }
            for dr in dag_states
        ]
        if dag_states and dag_states[-1].data_interval_start and dag_states[-1].data_interval_end:
            last_automated_data_interval = DataInterval(
                timezone.coerce_datetime(dag_states[-1].data_interval_start),
                timezone.coerce_datetime(dag_states[-1].data_interval_end),
            )
            year = last_automated_data_interval.end.year