To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. The variables are 'hours worked by John' and 'hours worked by Paul', which we can represent symbolically as \(x_1\) and \(x_2\), respectively.

The objective function is to minimize \(9x_1 + 8x_2\).

The constraints given can be translated into the following symbolic form:
- The total combined organization score: \(5x_1 + 4x_2 \geq 41\)
- The same constraint is repeated, so we consider it only once.
- The total combined paperwork competence rating: \(9x_1 + 2x_2 \geq 18\)
- Again, the same constraint is mentioned twice, so we keep only one instance.
- The total combined work quality rating: \(11x_1 + 3x_2 \geq 24\)
- The constraint on a linear combination of hours worked by John and Paul: \(7x_1 - 5x_2 \geq 0\)
- The upper bound for the organization score: \(5x_1 + 4x_2 \leq 93\)
- The upper bound for the paperwork competence rating: \(9x_1 + 2x_2 \leq 59\)
- The upper bound for the work quality rating: \(11x_1 + 3x_2 \leq 60\)

The symbolic representation of the problem is thus:
```json
{
    'sym_variables': [('x1', 'hours worked by John'), ('x2', 'hours worked by Paul')],
    'objective_function': '9*x1 + 8*x2',
    'constraints': [
        '5*x1 + 4*x2 >= 41',
        '9*x1 + 2*x2 >= 18',
        '11*x1 + 3*x2 >= 24',
        '7*x1 - 5*x2 >= 0',
        '5*x1 + 4*x2 <= 93',
        '9*x1 + 2*x2 <= 59',
        '11*x1 + 3*x2 <= 60'
    ]
}
```

To solve this optimization problem using Gurobi, we can use the following Python code:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(9*x1 + 8*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x1 + 4*x2 >= 41, name="organization_score_lower_bound")
m.addConstr(9*x1 + 2*x2 >= 18, name="paperwork_competence_rating_lower_bound")
m.addConstr(11*x1 + 3*x2 >= 24, name="work_quality_rating_lower_bound")
m.addConstr(7*x1 - 5*x2 >= 0, name="linear_combination_constraint")
m.addConstr(5*x1 + 4*x2 <= 93, name="organization_score_upper_bound")
m.addConstr(9*x1 + 2*x2 <= 59, name="paperwork_competence_rating_upper_bound")
m.addConstr(11*x1 + 3*x2 <= 60, name="work_quality_rating_upper_bound")

# Optimize the model
m.optimize()

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