**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
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={