Standardize(x) comes before drop na

It looks like Standardize(x) comes before drop_na --> therefore the standardization returns all nan

import numpy as np
import pandas as pd
from patsy import dmatrix

df = pd.DataFrame({'x1': np.arange(5), 'x2': [1,2,np.nan,5,6]})

output:
dmatrix("~x1+x2", df)
[[1. 0. 1.]
[1. 1. 2.]
[1. 3. 5.]
[1. 4. 6.]]

dmatrix("~x1+standardize(x2)", df)
DesignMatrix with shape (0, 3)
Intercept x1 standardize(x2)
Terms:
'Intercept' (column 0)
'x1' (column 1)
'standardize(x2)' (column 2)

Start by reproducing the two dmatrix examples from the issue and compare the handling of missing x2 values in drop_na and standardize(x2). Trace the standardize and missing-data processing entry points, then add a regression test showing the expected non-empty design matrix when standardization is used with missing data.