"thin" argument affects diagnostics

Describe the bug
If I use the same seed (to ensure reproducibility) and do two model$fit() runs with identical arguments except that one has thin=4, I get fewer divergent transitions, fewer max_treedepth transitions, and worse EBFMI reported for the run that has thinning. (Probably an underlying Stan issue, not just cmdstanr)

To Reproduce

model <- cmdstanr::cmdstan_model(path_to_funnel_model)

fit1 <- model$sample(data=list(), seed=12345678, chains=1, iter_warmup=1000, iter_sampling=4000)
fit1$diagnostic_summary()
# ^^^ num_divergent = 4, num_max_treedepth = 2, ebfmi = 0.08203262

fit2 <- model$sample(data=list(), seed=12345678, chains=1, iter_warmup=1000, iter_sampling=4000, thin=4)
fit2$diagnostic_summary()
# ^^^ num_divergent = 0, num_max_treedepth = 1, ebfmi = 0.2993997

Expected behavior
Thinning should only affect the number of draws saved, not the diagnostics. A divergent transition is an indication of a possible problem whether or not you save it.

Operating system
Your operating system (e.g. mac os x 10.15, windows 10, etc.)

CmdStanR version number
Your CmdStanR version number (e.g. from packageVersion("cmdstanr")).

Additional context
cmdstanr version 0.6.0.9000
R version 4.2.2
macOS 13.4 (Ventura)

Run the two model$sample reproductions with identical seeds and compare their diagnostic_summary() results. Trace how thin is handled between sampling and diagnostics, then verify that divergent transitions, max_treedepth transitions, and EBFMI are unchanged except for the saved draw count.