Theoretically, nothing precludes the use of $\lambda$-returns in actor-critic methods. The $\lambda$-return is an unbiased estimator of the Monte Carlo (MC) return, which means they are essentially interchangeable. In fact, as discussed in High-Dimensional Continuous Control Using Generalized Advantage Estimation, using the $\lambda$-return instead of the MC return can actually help reduce the variance of gradient updates.
The above is similar to my answer in the other question you linked, so let me try to answer your question more specifically. Even though we can use $\lambda$-returns, why are they not too common in practice? I suspect there might be a few reasons:
Empirically, faster credit assignment might be more desirable than lower variance. Sometimes the learning speed of your algorithm is constrained simply by how quickly you can learn about the consequences of certain actions. In this case, it is faster to use the MC return, even if it theoretically has higher variance than the $\lambda$-return.
When proposing a new algorithm, adding $\lambda$-returns to it might give it an "unfair" advantage if the other baseline methods do not use them (the reviewers would not like this), so researchers tend to favor simpler 1-step or MC returns for the sake of consistency. I would guess this is why you don't typically see $\lambda$-returns in papers that propose new actor-critic methods. In some sense, it is generally assumed that you could always add $\lambda$-returns to them later and probably get better performance.$^*$
A decent number of deep RL researchers don't know what $\lambda$-returns are. This is especially true if they come from a pure deep learning background; they may have never read Reinforcement Learning: An Introduction which is where most people are introduced to TD($\lambda$) and $\lambda$-returns.
$^*$Exceptions to this are papers like mine (Reconciling $\lambda$-Returns with Experience Replay) where the contribution is the use of $\lambda$-returns in methods that previously could not use them. But actor-critic methods are pretty straightforward to combine with $\lambda$-returns.