A model selected by ARIMA() from a pool of models cannot be fitted individually

Hello:
I would like to report an issue with ARIMA().
Please unzip the attached data first.

train_data.zip

library(fable)
load("train_data.Rdata")

# There are only two models in the pool, with and w/o the intercept.
# ARIMA selects the one w/o the intercept.
train_data |> 
  model(ARIMA(MW_Hourly ~ pdq(p = 1, d = 0, q = 2) + PDQ(P = 4, D = 1, Q = 1) + lag(HumidityLow, 24) + IsWeekend, 
              order_constraint = TRUE, stepwise = FALSE, greedy = FALSE, approximation = FALSE)) |>
              report()

# Series: MW_Hourly 
# Model: LM w/ ARIMA(1,0,2)(4,1,1)[24] errors 
# 
# Coefficients:

#   ar1     ma1      ma2     sar1     sar2     sar3     sar4     sma1  lag(HumidityLow, 24)  IsWeekendTRUE
# 0.9638  0.2247  -0.1032  -0.2097  -0.2230  -0.1849  -0.1512  -0.5088               -1.5179       -97.3029
# s.e.  0.0087  0.0320   0.0318   0.1012   0.0714   0.0571   0.0471   0.1030                1.6111        42.0355
# 
# sigma^2 estimated as 32392:  log likelihood=-7477.02
# AIC=14976.04   AICc=14976.27   BIC=15031.58

# Since the intercept was excluded, we try to specify that explicitly, to have only one model in the pool.
# However, it fails:

train_data |> 
  model(ARIMA(MW_Hourly ~ 0 + pdq(p = 1, d = 0, q = 2) + PDQ(P = 4, D = 1, Q = 1) + lag(HumidityLow, 24) + IsWeekend, 
              order_constraint = TRUE, stepwise = FALSE, greedy = FALSE, approximation = FALSE)) |>
  report()

# Series: MW_Hourly 
# Model: NULL model 
# NULL modelWarning message:
#   1 error encountered for ARIMA(MW_Hourly ~ 0 + pdq(p = 1, d = 0, q = 2) + PDQ(P = 4, D = 1, 
#                                                                                Q = 1) + lag(HumidityLow, 24) + IsWeekend, order_constraint = TRUE, 
#                                 stepwise = FALSE, greedy = FALSE, approximation = FALSE)
# [1] non-finite value supplied by optim

Unzip train_data.zip and run the two ARIMA() reproductions in R, comparing the automatically selected model with the explicit no-intercept specification. Trace why the explicit model produces a NULL model and “non-finite value supplied by optim”; done means the no-intercept model fits individually instead of failing.