To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for the quantities of interest (hours worked by Paul and Jean), formulating the objective function using these variables, and then expressing all constraints in terms of these variables.

Let's denote:
- \(x_1\) as the hours worked by Paul,
- \(x_2\) as the hours worked by Jean.

The objective function is to minimize \(8.11x_1 + 8.33x_2\).

Constraints are as follows:
1. Total combined productivity rating: \(1.18x_1 + 3.74x_2 \geq 40\)
2. Total combined work quality rating: \(6.04x_1 + 5.88x_2 \geq 89\)
3. Total combined organization score: \(1.18x_1 + 6.64x_2 \geq 33\)
4. Linear constraint: \(2x_1 - 3x_2 \geq 0\)
5. Upper bound on total productivity rating: \(1.18x_1 + 3.74x_2 \leq 133\)
6. Upper bound on total work quality rating: \(6.04x_1 + 5.88x_2 \leq 195\)
7. Upper bound on total organization score: \(1.18x_1 + 6.64x_2 \leq 110\)

Given these definitions, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'hours worked by Paul'), ('x2', 'hours worked by Jean')],
    'objective_function': '8.11*x1 + 8.33*x2',
    'constraints': [
        '1.18*x1 + 3.74*x2 >= 40',
        '6.04*x1 + 5.88*x2 >= 89',
        '1.18*x1 + 6.64*x2 >= 33',
        '2*x1 - 3*x2 >= 0',
        '1.18*x1 + 3.74*x2 <= 133',
        '6.04*x1 + 5.88*x2 <= 195',
        '1.18*x1 + 6.64*x2 <= 110'
    ]
}
```

To solve this problem using Gurobi, we can write the following Python code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="hours_worked_by_Paul", lb=0)
x2 = m.addVar(name="hours_worked_by_Jean", lb=0)

# Set the objective function
m.setObjective(8.11*x1 + 8.33*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(1.18*x1 + 3.74*x2 >= 40, name="productivity_rating")
m.addConstr(6.04*x1 + 5.88*x2 >= 89, name="work_quality_rating")
m.addConstr(1.18*x1 + 6.64*x2 >= 33, name="organization_score")
m.addConstr(2*x1 - 3*x2 >= 0, name="linear_constraint")
m.addConstr(1.18*x1 + 3.74*x2 <= 133, name="upper_bound_productivity")
m.addConstr(6.04*x1 + 5.88*x2 <= 195, name="upper_bound_work_quality")
m.addConstr(1.18*x1 + 6.64*x2 <= 110, name="upper_bound_organization")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Paul: {x1.x}")
    print(f"Hours worked by Jean: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```