Incorrect predictions from fit_resamples() when applied to lmer model

I am having trouble using the tune::fit_resamples() function on a lmer model (from the multilevelmod package).
In particular, it looks like that when the predictions for the assessment set are calculated, the model doesn't properly account for all the possible combinations of grouping levels.

I have included a reprex in which I show that the results of a predict() call on a lmer object are different than the predictions obtained from a fit_resamples() call (using collect_predictions()).

library(tidyverse)
library(tidymodels)
library(multilevelmod)

data(mpg, package = "ggplot2")

set.seed(123)

lmer_model = linear_reg() %>% 
  set_engine("lmer")

lmer_workflow = workflow() %>% 
  add_variables(outcomes = cty,
                predictors = c(year, manufacturer, model)) %>% 
  add_model(lmer_model, formula = cty ~ year + (1|manufacturer/model))

mpg_split = mpg %>% validation_split(prop = 3/4)

analysis = mpg_split$splits[[1]] %>% analysis()
assessment = mpg_split$splits[[1]] %>% assessment()

# using predict() on the assessment dataset works as expected
predicted_via_workflow = lmer_workflow %>%
  fit(analysis) %>%
  extract_fit_engine() %>%
  predict(assessment) %>%
  plot()

# the predictions from the fit_resamples() function do not vary per group
predicted_via_tune = lmer_workflow %>% 
  fit_resamples(mpg_split, control = control_resamples(allow_par = FALSE,
                                                       save_pred = TRUE)) %>% 
  collect_predictions() %>%
  pluck(".pred") %>%
  plot()

^{Created on 2022-06-20 by the reprex package (v2.0.1)}

Start by reproducing the discrepancy between predict() on the fitted lmer engine and fit_resamples() followed by collect_predictions() using the supplied mpg workflow and validation split. Trace how assessment-set predictions handle grouping-level combinations; done when fit_resamples() produces group-varying predictions consistent with direct predict() results.