### 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)

x_points[first_ti.task_id].append(date_time)
                fails_dict_key = (first_ti.dag_id, first_ti.task_id, first_ti.run_id)
                fails_total = fails_totals[fails_dict_key]
                y_points[first_ti.task_id].append(float(duration + fails_total))
        cumulative_y = {k: list(itertools.accumulate(v)) for k, v in y_points.items()}
        
        
        y_unit = infer_time_unit([d for t in y_points.values() for d in t])
        cum_y_unit = infer_time_unit([d for t in cumulative_y.values() for d in t])
        
        chart.create_y_axis("yAxis", format=".02f", custom_format=False, label=f"Duration ({y_unit})")
        chart.axislist["yAxis"]["axisLabelDistance"] = "-15"
        cum_chart.create_y_axis("yAxis", format=".02f", custom_format=False, label=f"Duration ({cum_y_unit})")
        cum_chart.axislist["yAxis"]["axisLabelDistance"] = "-15"
        for task_id in x_points:
            chart.add_serie(
                name=task_id + "[]" if task_id in mapped_tis else task_id,
                x=x_points[task_id],
                y=scale_time_units(y_points[task_id], y_unit),
            )
            cum_chart.add_serie(
                name=task_id + "[]" if task_id in mapped_tis else task_id,
                x=x_points[task_id],
                y=scale_time_units(cumulative_y[task_id], cum_y_unit),
            )
        dates = sorted({ti.execution_date for ti in task_instances})
        max_date = max(ti.execution_date for ti in task_instances) if dates else None
        session.commit()
        form = DateTimeWithNumRunsForm(
            data={