Inconsistent interpolation based on data typed

What happened?

Depending on the data type, interpolate gives different results

What did you expect to happen?

The example code outputs two arrays which are pasted below. The first array end with a one while the seconds array, which is based on the float32 dataset, has all NaN. I was expecting each array to have 6 NaNs and one numerical value.

[nan nan nan nan nan nan 1.]
[nan nan nan nan nan nan nan]

Minimal Complete Verifiable Example

import xarray as xr
import numpy as np
import pandas as pd

time_range = pd.date_range(start='2024-02-20T12', periods=2, freq='6H')

data1 = xr.DataArray([np.nan,1], dims='time', coords={'time': time_range})
data2 = data1.astype('float32')

print(data1.resample({"time":"1H"}).interpolate("linear").values)
print(data2.resample({"time":"1H"}).interpolate("linear").values)

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.
• Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

No response

Environment

Start by running the minimal Python example with the listed xarray, NumPy, pandas, and SciPy versions, then compare interpolation for the default and float32 arrays. Trace the resample/interpolate entry point and add a regression test showing that both outputs contain six NaNs followed by 1.