**Apache Airflow version**:
2.1.0

**What happened**:
There are several hooks that still use optional parameters from the classic `Extra` field.  However, when creating the connection the `Extra` field is overwritten with values from the custom fields that are included in the form.  Because the `Extra` field is overwritten, these optional parameters cannot be used by the hook.

For example, in the `AzureDataFactoryHook`, If `resource_group_name` or `factory_name` are not provided when initializing the hook, it defaults to the value specified in the connection extras. Using the Azure Data Factory connection form, here is the initial connection submission:
![image](https://user-images.githubusercontent.com/48934154/127029091-a8b1c979-f238-4bac-9d3a-f94b6cd92a2b.png)

After saving the connection, the `Extra` field is overwritten with the custom fields that use "extras" under the hood:
![image](https://user-images.githubusercontent.com/48934154/127029242-79c990f0-b9cf-43bb-9a6b-28b4e9c6d4e1.png)


**What you expected to happen**:

Wavering slightly but I would have initially expected that the `Extra` field wasn't overwritten but updated with both the custom field "extras" plus the originally configured values in the `Extra` field.  However, a better UX would be that the values used in the `Extra` field should be separate custom fields for these hooks and the `Extra` field is hidden.  Perhaps it's even both?

**How to reproduce it**:
Install either the Microsoft Azure or Snowflake providers, attempt to create a connection for either the Snowflake, Azure Data Factory, Azure Container Volume, or Azure types with the `Extra` field populated prior to saving the form.


**Anything else we need to know**:
Happy to submit PRs to fix this issue. 🚀 


airflow/www/views.py
"""
    Create the list of edges needed to construct the Graph view.
    A special case is made if a TaskGroup is immediately upstream/downstream of another
    TaskGroup or task. Two dummy nodes named upstream_join_id and downstream_join_id are
    created for the TaskGroup. Instead of drawing an edge onto every task in the TaskGroup,
    all edges are directed onto the dummy nodes. This is to cut down the number of edges on
    the graph.
    For example: A DAG with TaskGroups group1 and group2:
        group1: task1, task2, task3
        group2: task4, task5, task6
    group2 is downstream of group1:
        group1 >> group2
    Edges to add (This avoids having to create edges between every task in group1 and group2):
        task1 >> downstream_join_id
        task2 >> downstream_join_id
        task3 >> downstream_join_id
        downstream_join_id >> upstream_join_id
        upstream_join_id >> task4
        upstream_join_id >> task5
        upstream_join_id >> task6
    """
    
    edges_to_add = set()
    
    edges_to_skip = set()
    task_group_map = dag.task_group.get_task_group_dict()
    def collect_edges(task_group):