### 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)

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={