combine_by_coords can succed when it shouldn't

What happened:

combine_by_coords can succeed when it should not - depending on the name of the dimensions (which determines the order of operations in combine_by_coords).

What you expected to happen:

• I think it should throw an error in both cases.

Minimal Complete Verifiable Example:

import numpy as np
import xarray as xr


data = np.arange(5).reshape(1, 5)
x = np.arange(5)
x_name = "lat"

da0 = xr.DataArray(data, dims=("t", x_name), coords={"t": [1], x_name: x}).to_dataset(name="a")
x = x + 1e-6
da1 = xr.DataArray(data, dims=("t", x_name), coords={"t": [2], x_name: x}).to_dataset(name="a")
ds = xr.combine_by_coords((da0, da1))

ds

returns:

<xarray.Dataset>
Dimensions:  (lat: 10, t: 2)
Coordinates:
  * lat      (lat) float64 0.0 1e-06 1.0 1.0 2.0 2.0 3.0 3.0 4.0 4.0
  * t        (t) int64 1 2
Data variables:
    a        (t, lat) float64 0.0 nan 1.0 nan 2.0 nan ... 2.0 nan 3.0 nan 4.0

Thus lat is interlaced - it don't think combine_by_coords should do this. If you set

x_name = "lat"

and run the example again, it returns:

ValueError: Resulting object does not have monotonic global indexes along dimension x

Anything else we need to know?:

• this is vaguely related to #4077 but I think it is separate
• combine_by_coords concatenates over all dimensions where the coords are different - therefore compat="override" doesn't actually do anything? Or does it?

https://github.com/pydata/xarray/blob/ba42c08af9afbd9e79d47bda404bf4a92a7314a0/xarray/core/combine.py#L69

cc @dcherian @TomNicholas

Environment:

Start at xarray/core/combine.py around the combine_by_coords logic linked in the issue, and reproduce the minimal example with both dimension-name cases. Trace why coordinate ordering changes the result, then add regression coverage showing that interlaced coordinates are rejected consistently. Done means both cases raise the expected ValueError without changing valid combinations.