My design consists of a blocking variable and two factors of interest. We collected samples from three plots repeatedly over time (5 time points), but we are not interested in the effects of plot and as such would like to control for it. From each plot, we collected two different types of sample.

colData(dds)
   plot time sample_type
1     A    1        soil
2     A    2        soil
3     A    3        soil
4     A    4        soil
5     A    5        soil
6     A    1        soil
7     A    2        soil
8     A    3        soil
9     A    4        soil
10    A    5        soil
11    B    1        soil
12    B    2        soil
13    B    3        soil
14    B    4        soil
15    B    5        soil
16    B    1       plant
17    B    2       plant
18    B    3       plant
19    B    4       plant
20    B    5       plant
21    C    1       plant
22    C    2       plant
23    C    3       plant
24    C    4       plant
25    C    5       plant
26    C    1       plant
27    C    2       plant
28    C    3       plant
29    C    4       plant
30    C    5       plant

Our design is ~plot + sample_type + time + sample_type:time

resultsNames(dds)
'Intercept' 'plot_2_vs_1''plot_3_vs_1''sample_type_soil_vs_plant''time_point_2_vs_1''time_point_3_vs_1''time_point_4_vs_1''time_point_5_vs_1''sample_typesoil.time_point2''sample_typesoil.time_point3''sample_typesoil.time_point4''sample_typesoil.time_point5'

I am confused about interpreting these terms, starting with 'sample_type_soil_vs_plant'. Given that I have not included plot in the interaction, I am currently inclined to interpret this as the FC difference between soil vs plant at time point 1, after controlling for plot or in other words its averaged across the three plots. Would appreciate any advice if my interpretation is correct here?

You interpretation is correct.