## Step 1: Define the symbolic representation of the variables
The variables are ['hours worked by Laura', 'hours worked by Paul', 'hours worked by Jean', 'hours worked by Dale']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $5x_1 + 3x_2 + 5x_3 + 9x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $2x_1 \leq 89$ (Laura's organization score is 2, but it seems like it should be an equality or a direct assignment, however, based on the given resource/attributes, it seems $x_0$ is not a variable but a constant, so we consider it as $2$ directly for Laura, $5$ for Paul, $5$ for Jean, and $11$ for Dale),
- $5x_2 \leq 89$,
- $5x_3 \leq 89$,
- $11x_4 \leq 89$,
- $5x_3 + 11x_4 \geq 22$,
- $2x_1 + 11x_4 \geq 13$,
- $2x_1 + 5x_3 \geq 17$,
- $2x_1 + 5x_2 + 5x_3 \geq 15$,
- $5x_2 + 5x_3 \leq 51$,
- $2x_1 + 11x_4 \leq 72$,
- $2x_1 + 5x_2 \leq 62$,
- $2x_1 + 5x_2 + 5x_3 + 11x_4 \leq 62$.

## 4: Correcting Understanding of Constraints
Given the attributes {'r0': {'description': 'organization score', 'upper_bound': 89, 'x0': 2, 'x1': 5, 'x2': 5, 'x3': 11}}, it seems that each person has a fixed organization score per hour worked: Laura = 2, Paul = 5, Jean = 5, Dale = 11.

## 5: Formulate the Problem with Correct Constraints
The problem then can be seen as:
- Maximize $5x_1 + 3x_2 + 5x_3 + 9x_4$
- Subject to:
- $x_2 \in \mathbb{Z}$ (integer hours worked by Paul),
- $x_3 \in \mathbb{Z}$ (integer hours worked by Jean),
- $x_4 \in \mathbb{Z}$ (integer hours worked by Dale),
- $x_1 \in \mathbb{R}$ (real hours worked by Laura),
- $5x_3 + 11x_4 \geq 22$,
- $2x_1 + 11x_4 \geq 13$,
- $2x_1 + 5x_3 \geq 17$,
- $2x_1 + 5x_2 + 5x_3 \geq 15$,
- $5x_2 + 5x_3 \leq 51$,
- $2x_1 + 11x_4 \leq 72$,
- $2x_1 + 5x_2 \leq 62$,
- $2x_1 + 5x_2 + 5x_3 + 11x_4 \leq 62$.

## 6: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'hours worked by Laura'), ('x2', 'hours worked by Paul'), ('x3', 'hours worked by Jean'), ('x4', 'hours worked by Dale')],
    'objective_function': '5*x1 + 3*x2 + 5*x3 + 9*x4',
    'constraints': [
        '5*x3 + 11*x4 >= 22',
        '2*x1 + 11*x4 >= 13',
        '2*x1 + 5*x3 >= 17',
        '2*x1 + 5*x2 + 5*x3 >= 15',
        '5*x2 + 5*x3 <= 51',
        '2*x1 + 11*x4 <= 72',
        '2*x1 + 5*x2 <= 62',
        '2*x1 + 5*x2 + 5*x3 + 11*x4 <= 62'
    ]
}
```

## 7: Gurobi Code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=0, name="hours_worked_by_Laura", vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(lb=0, name="hours_worked_by_Paul", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(lb=0, name="hours_worked_by_Jean", vtype=gurobi.GRB.INTEGER)
    x4 = model.addVar(lb=0, name="hours_worked_by_Dale", vtype=gurobi.GRB.INTEGER)
    
    # Define objective function
    model.setObjective(5*x1 + 3*x2 + 5*x3 + 9*x4, gurobi.GRB.MAXIMIZE)
    
    # Define constraints
    model.addConstr(5*x3 + 11*x4 >= 22, name="constraint_1")
    model.addConstr(2*x1 + 11*x4 >= 13, name="constraint_2")
    model.addConstr(2*x1 + 5*x3 >= 17, name="constraint_3")
    model.addConstr(2*x1 + 5*x2 + 5*x3 >= 15, name="constraint_4")
    model.addConstr(5*x2 + 5*x3 <= 51, name="constraint_5")
    model.addConstr(2*x1 + 11*x4 <= 72, name="constraint_6")
    model.addConstr(2*x1 + 5*x2 <= 62, name="constraint_7")
    model.addConstr(2*x1 + 5*x2 + 5*x3 + 11*x4 <= 62, name="constraint_8")
    
    # Solve the model
    model.optimize()
    
    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Hours worked by Laura: ", x1.varValue)
        print("Hours worked by Paul: ", x2.varValue)
        print("Hours worked by Jean: ", x3.varValue)
        print("Hours worked by Dale: ", x4.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```