**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
TaskInstance.run_id,
            TaskInstance.state,
            TaskInstance._try_number,
            func.min(TaskInstanceNote.content).label("note"),
            func.count(func.coalesce(TaskInstance.state, sqla.literal("no_status"))).label("state_count"),
            func.min(TaskInstance.start_date).label("start_date"),
            func.max(TaskInstance.end_date).label("end_date"),
        )
        .join(TaskInstance.task_instance_note, isouter=True)
        .filter(
            TaskInstance.dag_id == dag.dag_id,
            TaskInstance.run_id.in_([dag_run.run_id for dag_run in dag_runs]),
        )
        .group_by(TaskInstance.task_id, TaskInstance.run_id, TaskInstance.state, TaskInstance._try_number)
        .order_by(TaskInstance.task_id, TaskInstance.run_id)
    )
    grouped_tis = {task_id: list(tis) for task_id, tis in itertools.groupby(query, key=lambda ti: ti.task_id)}
    def task_group_to_grid(item, grouped_tis, *, is_parent_mapped: bool):
        if isinstance(item, AbstractOperator):
            def _get_summary(task_instance):
                return {
                    "task_id": task_instance.task_id,
                    "run_id": task_instance.run_id,
                    "state": task_instance.state,
                    "start_date": task_instance.start_date,
                    "end_date": task_instance.end_date,
                    "try_number": wwwutils.get_try_count(task_instance._try_number, task_instance.state),
                    "note": task_instance.note,
                }
            def _mapped_summary(ti_summaries):