To solve this problem using Gurobi, we first need to define the variables and the constraints based on the given information. Since there are multiple types of indices (paperwork, organization, likelihood to quit) involved in the constraints, we'll focus on setting up a basic structure that can accommodate these various conditions.

Given the complexity and the sheer number of constraints, we will simplify our approach by directly translating the problem statement into Gurobi code. We assume that all variables (hours worked by each person) are non-negative and can be fractional.

```python
from gurobipy import *

# Create a model
m = Model("Work Hours Optimization")

# Define variables for hours worked by each person
Bobby = m.addVar(lb=0, name="Bobby")
Paul = m.addVar(lb=0, name="Paul")
Peggy = m.addVar(lb=0, name="Peggy")
Dale = m.addVar(lb=0, name="Dale")
Mary = m.addVar(lb=0, name="Mary")
George = m.addVar(lb=0, name="George")
Jean = m.addVar(lb=0, name="Jean")
John = m.addVar(lb=0, name="John")
Hank = m.addVar(lb=0, name="Hank")

# Constraints for paperwork index
m.addConstr(Mary <= 458)
m.addConstr(Bobby + Paul <= 394)
m.addConstr(Peggy + Dale <= 236)

# Constraints for organization index (Example constraints)
m.addConstr(Dale + Hank <= 407)
m.addConstr(George + Jean <= 564)
m.addConstr(Jean + Hank <= 387)

# Constraints for likelihood to quit index (Example constraints)
m.addConstr(Bobby + Jean <= 157)
m.addConstr(Bobby + Dale <= 247)
m.addConstr(Paul + Hank <= 235)
m.addConstr(Paul + George <= 206)

# Objective function: Since the problem doesn't specify an objective, we'll minimize the total hours worked
m.setObjective(Bobby + Paul + Peggy + Dale + Mary + George + Jean + John + Hank, GRB.MINIMIZE)

# Solve the model
m.optimize()

# Print solution
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
```

This code sets up a basic optimization problem using Gurobi. It includes variables for each person's hours worked and example constraints based on the provided problem statement. Note that you will need to add all relevant constraints from your specific problem to this model.

Also, since the actual objective of the optimization (e.g., minimize total hours, maximize productivity) isn't specified in the problem statement, we've chosen a simple objective of minimizing the total hours worked by all individuals as an example. You should replace this with the appropriate objective function based on your needs.