### Apache Airflow version

2.4.1

### What happened

When using dataset scheduling, it isn't obvious which datasets a downstream dataset consumer is awaiting in order for the DAG to be scheduled.

I would assume that this is supposed to be solved by the `Latest Update` column in the modal that opens when selecting `x of y datasets updated`, but it appears that the data isn't being populated.

<img width="601" alt="image" src="https://user-images.githubusercontent.com/5778047/194116186-d582cede-c778-47f7-8341-fc13a69a2358.png">

Although one of the datasets has been produced, there is no data in the `Latest Update` column of the modal.

In the above example, both datasets have been produced > 1 time.

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116368-ceff241f-a623-4893-beb7-637b821c4b53.png">

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116410-19045f5a-8400-47b0-afcb-9fbbffca26ee.png">


### What you think should happen instead

The `Latest Update` column should be populated with the latest update timestamp for each dataset required to schedule a downstream, dataset consuming DAG.

Ideally there would be some form of highlighting on the "missing" datasets for quick visual feedback when DAGs have a large number of datasets required for scheduling.

### How to reproduce

1. Create a DAG (or 2 individual DAGs) that produces 2 datasets
2. Produce both datasets
3. Then produce _only one_ dataset
4. Check the modal by clicking from the home screen on the `x of y datasets updated` button.

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

_No response_

### Deployment

Docker-Compose

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

"max_active_runs": max_active_runs,
                }
            )
        return flask.json.jsonify(payload)
    def _mark_dagrun_state_as_failed(self, dag_id, dag_run_id, confirmed):
        if not dag_run_id:
            return {"status": "error", "message": "Invalid dag_run_id"}
        dag = get_airflow_app().dag_bag.get_dag(dag_id)
        if not dag:
            return {"status": "error", "message": f"Cannot find DAG: {dag_id}"}
        new_dag_state = set_dag_run_state_to_failed(dag=dag, run_id=dag_run_id, commit=confirmed)
        if confirmed:
            return {"status": "success", "message": f"Marked failed on {len(new_dag_state)} task instances"}
        else:
            details = [str(t) for t in new_dag_state]
            return htmlsafe_json_dumps(details, separators=(",", ":"))
    def _mark_dagrun_state_as_success(self, dag_id, dag_run_id, confirmed):
        if not dag_run_id:
            return {"status": "error", "message": "Invalid dag_run_id"}
        dag = get_airflow_app().dag_bag.get_dag(dag_id)
        if not dag:
            return {"status": "error", "message": f"Cannot find DAG: {dag_id}"}
        new_dag_state = set_dag_run_state_to_success(dag=dag, run_id=dag_run_id, commit=confirmed)
        if confirmed:
            return {"status": "success", "message": f"Marked success on {len(new_dag_state)} task instances"}
        else:
            details = [str(t) for t in new_dag_state]
            return htmlsafe_json_dumps(details, separators=(",", ":"))
    @provide_session
    def _mark_dagrun_state_as_queued(self, dag_id: str, dag_run_id: str, confirmed: bool, session=None):