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Fix `time_bnds` and fix for Pandas 3 (#2310)

### What kind of change does this PR introduce?

#### `time_bnds`
While fixing a Pandas v3 error, I also revisited the `time_bnds`
function. I'm not sure what I had in mind when implementing it, but it
was not following the CF conventions.

Example:
```python3
import xclim as xc
import xarray as xr

time = xr.date_range('2000-01-01', freq='MS', periods=3)
xc.core.calendar.time_bnds(time, 'MS')
```
Before: the end is the last timestep (up to a precision) before the next
period
```
[['2000-01-01T00:00:00.000000000', '2000-01-31T23:59:59.999999999'],
 ['2000-02-01T00:00:00.000000000', '2000-02-29T23:59:59.999999999'],
 ['2000-03-01T00:00:00.000000000', '2000-03-31T23:59:59.999999999']]
```
After: the end is the same as the start of the next period
```
[['2000-01-01T00:00:00.000000000', '2000-02-01T00:00:00.000000000'],
 ['2000-02-01T00:00:00.000000000', '2000-03-01T00:00:00.000000000'],
 ['2000-03-01T00:00:00.000000000', '2000-04-01T00:00:00.000000000']]
```

The `precision` argument as thus been removed.


#### Pandas v3
We had two other Pandas v3 issues:

In `aggregate_between_dates` we were modifying an array in-place
(`days[days < 0] = np.nan`) and this seems not to be possible anymore.
However, it has always been discouraged by xarray, so I changed it to a
`days.where(days >=0)`.

In the partitioning functions of `xclim.ensembles` we were creating new
coordinate by passing a `pd.Index`, this seems to be broken now that
pandas has implemented a new String dtype with nan support. Numpy does
not recognize that. Xarray should probably fix this by having an
automated translation, but in the meantime it was easy to fix by
creating the coordinate with `xr.DataArray` instead.


### Does this PR introduce a breaking change?
Yes, the `time_bnds` result is now different, an argument has been
removed.
Source Files on build 21262906040
Detailed source file information is not available for this build.