singular fit in one simple model but not in another using lmer R

Question

I'm running a multilevel model using lmerTest in which employees are nested in teams and departments. I'm taking a model comparison approach, so I'm building the model with just the random effects. Here are the results when I use the two random effects (team and department membership) to predict intense exercise:

library(lme4)
summary(m0_ev_io <- lmer(exer_vig ~ 1 + (1 | team_num) + (1 | dept_client), data = clean_data_0))


Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: exer_vig ~ 1 + (1 | team_num) + (1 | dept_client)
   Data: clean_data_0

REML criterion at convergence: 527.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6783 -0.6071 -0.2324  0.4233  2.1587 

Random effects:
 Groups      Name        Variance Std.Dev.
 team_num    (Intercept) 0.16687  0.4085  
 dept_client (Intercept) 0.03047  0.1746  
 Residual                1.14821  1.0715  
Number of obs: 169, groups:  team_num, 58; dept_client, 33

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   2.6743     0.1081 14.6284   24.74  2.4e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

This model--and all subsequent ones--run well with no errors. However, when I run the same model using the same data on lite exercise, I get a singularity warning and suddenly department membership has no variance:

summary(m0_el_io <- lmer(exer_lite ~ 1 + (1 | team_num) + (1 | dept_client), data = clean_data_0))


boundary (singular) fit: see ?isSingular
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: exer_lite ~ 1 + (1 | team_num) + (1 | dept_client)
   Data: clean_data_0

REML criterion at convergence: 542

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6403 -0.5925 -0.3208  0.4440  2.0776 

Random effects:
 Groups      Name        Variance Std.Dev.
 team_num    (Intercept) 0.1471   0.3835  
 dept_client (Intercept) 0.0000   0.0000  
 Residual                1.3027   1.1414  
Number of obs: 169, groups:  team_num, 58; dept_client, 33

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   2.7160     0.1037 42.5453    26.2   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
convergence code: 0
boundary (singular) fit: see ?isSingular 

Except for the dependent variable, the data are the same, so I'm very confused why this would be the case. I'm confident it's not due to overfitting (like in this thread (How to cope with a singular fit in a linear mixed model (lme4)?)) because, even when the vigorous exercise model contains many more variables, it never gives a singular warning.

Do you have any ideas on why this is happening and how I can fix this issue without removing department membership? I've tried recommendations from other sites, including turning REML = FALSE and changing the optimizer [control = lmerControl(optimizer ='optimx', optCtrl=list(method='L-BFGS-B')], but nothing has worked.

Thank you!

EDIT: Here is a sample of the data. Note: team_num and dept_client are factors.

library(tidyverse)
clean_data_0 <- tibble(
  exer_lite = c(5, 4, 4, 5, 2, 4, 3, 1, 2, 2, 5,3, 4, 5, 2, 2, 2, 5, 5, 2, 3, 3, 1, 2, 5),
  exer_vig = c(4, 2, 4, 1, 2, 2, 3, 1, 2, 2, 5, 3, 3, 5, 2, 2, 3, 5, 5, 2, 3, 2, 1, 3, 5),
  dept_client = factor(c(17, 17, 45, 45, 80, 100, 90, 14, 2, 80, 100, 90, 121, 121, 121, 2, 90, 90, 90, 2, 100, 14, 14, 76, 76)),
  team_num = factor(c(509, 509, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 13, 13, 14, 14)),
  id = c(1:25))