## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and the objective function based on the given problem description.

## Step 2: Identify the variables and their corresponding symbolic notation
The variables are 'hours worked by John' and 'hours worked by Paul', which we can denote as $x_1$ and $x_2$ respectively.

## 3: Define the objective function in symbolic notation
The objective function to minimize is $9x_1 + 8x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $5x_1 \geq 5$ (John's organization score is 5 or more, but since it's an equality in the description, we'll consider it as $5x_1 = 5$)
- $9x_1 \geq 9$ (John's paperwork competence rating is 9 or more, but again, equality)
- $11x_1 \geq 11$ (John's work quality rating is 11 or more)
- $4x_2 \geq 4$ (Paul's organization score is 4 or more, equality)
- $2x_2 \geq 2$ (Paul's paperwork competence rating is 2 or more, equality)
- $3x_2 \geq 3$ (Paul's work quality rating is 3 or more)
- $5x_1 + 4x_2 \geq 41$ (total combined organization score)
- $9x_1 + 2x_2 \geq 18$ (total combined paperwork competence rating)
- $11x_1 + 3x_2 \geq 24$ (total combined work quality rating)
- $7x_1 - 5x_2 \geq 0$
- $5x_1 + 4x_2 \leq 113$ (upper bound for organization score)
- $9x_1 + 2x_2 \leq 60$ (upper bound for paperwork competence rating)
- $11x_1 + 3x_2 \leq 60$ (upper bound for work quality rating)

## 5: Correct and simplify constraints based on the problem description
Given that some constraints were initially misinterpreted as inequalities when described as equalities or specific values, let's correct that:
- The actual constraints from the description are:
  - $5x_1 = 5$
  - $9x_1 = 9$
  - $11x_1 = 11$
  - $4x_2 = 4$
  - $2x_2 = 2$
  - $3x_2 = 3$
  And the rest as previously listed.

## 6: Convert the problem into Gurobi code
Given the complexity and the specific request for format, let's directly formulate the Gurobi model.

## 7: Formulate the Gurobi model
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name='hours_worked_by_John', lb=0)  # Assuming non-negative hours
x2 = m.addVar(name='hours_worked_by_Paul', lb=0)

# Objective function
m.setObjective(9 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(5 * x1 == 5, name='John_organization_score')
m.addConstr(9 * x1 == 9, name='John_paperwork_competence_rating')
m.addConstr(11 * x1 == 11, name='John_work_quality_rating')
m.addConstr(4 * x2 == 4, name='Paul_organization_score')
m.addConstr(2 * x2 == 2, name='Paul_paperwork_competence_rating')
m.addConstr(3 * x2 == 3, name='Paul_work_quality_rating')

m.addConstr(5 * x1 + 4 * x2 >= 41, name='total_organization_score')
m.addConstr(9 * x1 + 2 * x2 >= 18, name='total_paperwork_competence_rating')
m.addConstr(11 * x1 + 3 * x2 >= 24, name='total_work_quality_rating')

m.addConstr(7 * x1 - 5 * x2 >= 0, name='hours_worked_constraint')

m.addConstr(5 * x1 + 4 * x2 <= 113, name='organization_score_upper_bound')
m.addConstr(9 * x1 + 2 * x2 <= 60, 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 solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Optimal solution found.')
    print('Hours worked by John: ', x1.varValue)
    print('Hours worked by Paul: ', x2.varValue)
    print('Objective: ', m.objVal)
else:
    print('No optimal solution found.')
```

## 8: Symbolic representation
```json
{
    'sym_variables': [
        ['x1', 'hours worked by John'],
        ['x2', 'hours worked by Paul']
    ],
    'objective_function': '9*x1 + 8*x2',
    'constraints': [
        '5*x1 = 5',
        '9*x1 = 9',
        '11*x1 = 11',
        '4*x2 = 4',
        '2*x2 = 2',
        '3*x2 = 3',
        '5*x1 + 4*x2 >= 41',
        '9*x1 + 2*x2 >= 18',
        '11*x1 + 3*x2 >= 24',
        '7*x1 - 5*x2 >= 0',
        '5*x1 + 4*x2 <= 113',
        '9*x1 + 2*x2 <= 60',
        '11*x1 + 3*x2 <= 60'
    ]
}
```