How should I interpret optimal (P,D,Q) values of (0,0,0) when running a SARIMA model?

Question

I run the auto_arima() function from the pmdarima package in Python on a time-series dataset with over two years worth of daily time-series data:

from pmdarima import auto_arima
model = auto_arima(df['Revenue'], trace=True, information_criterion='bic', m=12)
model.summary()

The result:

ARIMA(2,1,2)(1,0,1)[12] intercept   : BIC=919.388, Time=1.10 sec
ARIMA(0,1,0)(0,0,0)[12] intercept   : BIC=1025.158, Time=0.03 sec
ARIMA(1,1,0)(1,0,0)[12] intercept   : BIC=897.006, Time=0.08 sec/
ARIMA(0,1,1)(0,0,1)[12] intercept   : BIC=922.501, Time=0.16 sec
ARIMA(0,1,0)(0,0,0)[12]             : BIC=1020.074, Time=0.01 sec
ARIMA(1,1,0)(0,0,0)[12] intercept   : BIC=895.139, Time=0.03 sec
ARIMA(1,1,0)(0,0,1)[12] intercept   : BIC=897.228, Time=0.22 sec
ARIMA(1,1,0)(1,0,1)[12] intercept   : BIC=903.347, Time=0.39 sec
ARIMA(2,1,0)(0,0,0)[12] intercept   : BIC=901.672, Time=0.04 sec
ARIMA(1,1,1)(0,0,0)[12] intercept   : BIC=901.686, Time=0.10 sec
ARIMA(0,1,1)(0,0,0)[12] intercept   : BIC=919.979, Time=0.03 sec
ARIMA(2,1,1)(0,0,0)[12] intercept   : BIC=906.314, Time=0.18 sec
ARIMA(1,1,0)(0,0,0)[12]             : BIC=889.168, Time=0.02 sec
ARIMA(1,1,0)(1,0,0)[12]             : BIC=891.185, Time=0.05 sec
ARIMA(1,1,0)(0,0,1)[12]             : BIC=891.414, Time=0.08 sec
ARIMA(1,1,0)(1,0,1)[12]             : BIC=897.494, Time=0.14 sec
ARIMA(2,1,0)(0,0,0)[12]             : BIC=895.690, Time=0.03 sec
ARIMA(1,1,1)(0,0,0)[12]             : BIC=895.706, Time=0.05 sec
ARIMA(0,1,1)(0,0,0)[12]             : BIC=914.353, Time=0.05 sec
ARIMA(2,1,1)(0,0,0)[12]             : BIC=900.319, Time=0.20 sec

Best model:  ARIMA(1,1,0)(0,0,0)[12]

How should a (P,D,Q) value of (0,0,0) be interpreted? Does that mean auto_arima couldn't detect a seasonal component to the data?

I've attempted to run models with the same inputs with varying m period values and get the same BIC, AIC, and HQIC values.

When I attempt to run models with identical inputs, varying the m-period, I get identical information criterion results, even when m=1. This leads me to believe that a seasonal order of (0,0,0) translates to no seasonality accounted for in the model. Am I correct in that belief?