To solve the given optimization problem, we need to maximize the objective function $2x_0 + 9x_1$ subject to several constraints. Here's a breakdown of how these constraints translate into mathematical expressions:

- The organization scores for Bobby and Hank are 6 and 5, respectively.
- The total combined organization score from hours worked by both must be at least 14: $6x_0 + 5x_1 \geq 14$.
- Another constraint is $10x_0 - 9x_1 \geq 0$.
- The total combined organization score should also not exceed 39: $6x_0 + 5x_1 \leq 39$.

Given that the hours worked by both Bobby and Hank do not have to be integers, we treat $x_0$ (hours worked by Bobby) and $x_1$ (hours worked by Hank) as continuous variables.

Here's how we can set up this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define the variables
x0 = m.addVar(lb=0, name="hours_worked_by_Bobby")
x1 = m.addVar(lb=0, name="hours_worked_by_Hank")

# Set the objective function: Maximize 2*x0 + 9*x1
m.setObjective(2*x0 + 9*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(6*x0 + 5*x1 >= 14, name="min_organization_score")
m.addConstr(10*x0 - 9*x1 >= 0, name="additional_constraint")
m.addConstr(6*x0 + 5*x1 <= 39, name="max_organization_score")

# Optimize the model
m.optimize()

# Print out the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Bobby: {x0.x}")
    print(f"Hours worked by Hank: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```