Individual t tests are not going to be using variances pooled across more than two groups, like an ANOVA model does. So that's one difference. But regarding the the unexpected degrees of freedom, I don't think the issue is that estimated marginal means are being used, per se. Instead, I think the issue is the same as what was addressed in another fairly recent thread (that actually spanned across multiple threads in multiple forums--but here's the recent jamovi thread:

https://forum.jamovi.org/viewtopic.php?f=2&t=689 ).

As a understand it, the conclusions of that discussion are summarized as follows:

(1) For ANOVA post-hoc tests, jamovi uses Welch's t tests, not Student's t tests.(See

https://en.wikipedia.org/wiki/Welch%27s_t-test ).

(2) In the case of repeated-measures ANOVA, the full Welch method is used, including the Welch-Satterthwaite method for computing degrees of freedom for the post-hoc test. In that method, the degrees of freedom are influenced not just by the number of observations, but also by the observed variances. Again, see

https://en.wikipedia.org/wiki/Welch%27s_t-test .

(3) For the non-repeated-measures ANOVA, the post-hocs also use Welch's rather than Student's t, but it is a variation of the Welch method. In this variation, all the elements of the Welch method are used except that the Welch-Satterthwaite degrees of freedom are not used. Instead, degrees of freedom are calculated the way one would calculate them for Student's t.

(4) The differences in the way degrees of freedom are calculated in the repeated-measures ANOVA post-hocs versus the non-repeated-measures ANOVA post-hocs is due to the fact that jamovi's ANOVA routines make use of the EMMEANS package in R, and that the different R programmers who created the packages on which EMMEANS relies made different choices about how to calculate the degrees of freedom for the Welch's t tests.