### 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)

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):