It seems you are treating days (within treatments) as blocks. There might well be reasons for doing that, even if you don't told us them. So just let the data decide if there really is block differences, and use a mixed model, with random intercepts for the blocks. In R this could be written something like

mod0 <- lme4::lmer(Y ~ treatment + (1 | day:treatment), data=your_data_frame)
summary(mod0)

then look at the estimated variance of the random effect to see if there is evidence for block effects.

It seems you are treating days (within treatments) as blocks. There might well be reasons for doing that, even if you don't told us them. So just let the data decide if there really is block differences, and use a mixed model, with random intercepts for the blocks. In R this could be written something like

mod0 <- lme4::lmer(Y ~ treatment + (1 | day:treatment), 
                    data=your_data_frame)
summary(mod0)

then look at the estimated variance of the random effect to see if there is evidence for block effects.