### 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)

children_states = {item["state"] for item in child_instances}
            group_state = next((state for state in wwwutils.priority if state in children_states), None)
            group_start_date = min(filter(None, children_start_dates), default=None)
            group_end_date = max(filter(None, children_end_dates), default=None)
            return {
                "task_id": task_group.group_id,
                "run_id": dag_run.run_id,
                "state": group_state,
                "start_date": group_start_date,
                "end_date": group_end_date,
            }
        def get_mapped_group_summaries():
            mapped_ti_query = (
                session.query(
                    TaskInstance.task_id, TaskInstance.state, TaskInstance.run_id, TaskInstance.map_index
                )
                .filter(
                    TaskInstance.dag_id == dag.dag_id,
                    TaskInstance.task_id.in_(child["id"] for child in children),
                    TaskInstance.run_id.in_(r.run_id for r in dag_runs),
                )
                .order_by(TaskInstance.task_id, TaskInstance.run_id)
            )
            
            mapped_tis: Mapping[str, Mapping[int, list[TaskInstance]]] = collections.defaultdict(
                lambda: collections.defaultdict(list),
            )
            for ti in mapped_ti_query:
                mapped_tis[ti.run_id][ti.map_index].append(ti)
            def get_mapped_group_summary(run_id: str, mapped_instances: Mapping[int, list[TaskInstance]]):