To solve this optimization problem, we first need to translate the given natural language description into a symbolic representation. This involves identifying variables, the objective function, and constraints.

### Symbolic Representation

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

The objective function is to maximize: \(9x_1 + 2x_2\).

Given constraints:
1. Organization score constraint for Paul: \(14x_1\),
2. Work quality rating constraint for Paul: \(12x_1\),
3. Organization score constraint for Laura: \(18x_2\),
4. Work quality rating constraint for Laura: \(11x_2\),
5. Total combined organization score must be 33 or more: \(14x_1 + 18x_2 \geq 33\),
6. Total combined work quality rating must be 30 or more: \(12x_1 + 11x_2 \geq 30\),
7. Constraint involving hours worked by Paul and Laura: \(5x_1 - 7x_2 \geq 0\),
8. Maximum total combined organization score: \(14x_1 + 18x_2 \leq 68\),
9. Maximum total combined work quality rating: \(12x_1 + 11x_2 \leq 77\).

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'hours worked by Paul'), ('x2', 'hours worked by Laura')],
    'objective_function': '9*x1 + 2*x2',
    'constraints': [
        '14*x1 + 18*x2 >= 33',
        '12*x1 + 11*x2 >= 30',
        '5*x1 - 7*x2 >= 0',
        '14*x1 + 18*x2 <= 68',
        '12*x1 + 11*x2 <= 77'
    ]
}
```

### Gurobi Code

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Paul")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Laura")

# Set the objective function
m.setObjective(9*x1 + 2*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(14*x1 + 18*x2 >= 33, "organization_score")
m.addConstr(12*x1 + 11*x2 >= 30, "work_quality_rating")
m.addConstr(5*x1 - 7*x2 >= 0, "hours_constraint")
m.addConstr(14*x1 + 18*x2 <= 68, "max_organization_score")
m.addConstr(12*x1 + 11*x2 <= 77, "max_work_quality_rating")

# Optimize the model
m.optimize()

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