**Description**

Replace the Configuration JSON text-area with a form that has individual input fields for each key & value. This will remove the need for the user to provide a json object. See hashicorp vault's key-value secrets UI for an example of what this might look like:

![Screenshot 2020-09-21 at 12 06 03](https://user-images.githubusercontent.com/17148324/93759844-df04f680-fc02-11ea-8613-f3bcc0b22d09.png)
 
- If present these keys and values should be serialised as json client-side and posted to the trigger-dag API endpoint as the `conf` request-parameter.
- By default no conf request-parameter is sent. Perhaps a checkbox should be used to determine whether conf is sent at all?
- The user may want to add new keys, or remove keys & values that they have previously added.
- [PR-10839](https://github.com/apache/airflow/pull/10839) is pre-populating the form with the DAG's default params. The new form should be pre-populated with the keys & values from `DAG.params`


**Use case / motivation**

Currently when triggering a manual DAG run with user-provided runtime parameters, the user has to provide a correctly formatted JSON object. Filling in a form with keys & values will be a significantly better user experience, and will be less error prone & laborious than handling json in a free-text field.

This builds on the great work already done on enhancing the Trigger-DAG functionality

**Related Issues**

Originating PR: https://github.com/apache/airflow/pull/10839
.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
            restriction = TimeRestriction(dag.start_date, dag.end_date, False)
            dates = collections.Counter()
            if isinstance(dag.timetable, CronMixin):
                
                dates_iter: Iterator[datetime | None] = croniter(
                    dag.timetable._expression,
                    start_time=last_automated_data_interval.end,
                    ret_type=datetime,
                )