DESeq2 normalizes the reads for differences in library size using the geometric mean of counts. You can access the normalized counts using counts(dds, normalized=TRUE) after size factor estimation.

Regardless of method, the magnitude of the effect size from your example data is pretty low. For most downstream analysis, people choose a Log2 fold cutoff value on top of an adjust p-value threshold to only retain genes with a plausibly interesting effect size. This is also because p-value is partially related to the sample size due to the concept of statistical power and type II error (false negatives). The more samples you add, the better your ability to potentially detect smaller effect sizes in your sample, but whether these small effect sizes detected is interesting is an informed judgment call.