The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which supposes the lack of an interaction.
In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order ot accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships amogn the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allows for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.
Bollen & Paxton (1998) and Muthen & Asperouhov (2015) both demonstrate interactions in path models with latent variables, but these interaction explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also see diagram similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.