## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Dale', 'hours worked by Jean', 'hours worked by Bill', and 'hours worked by Laura', which can be represented symbolically as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $7.52x_0 + 5.41x_1 + 2.72x_2 + 9.75x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $4.64x_0 \leq 195$ (not directly given but implied as an upper bound for paperwork)
- $6.34x_0 \leq 171$ (not directly given but implied as an upper bound for productivity)
- $8.08x_1 \leq 195$
- $4.69x_1 \leq 171$
- $4.12x_2 \leq 195$
- $5.92x_2 \leq 171$
- $0.75x_3 \leq 195$
- $4.45x_3 \leq 171$
- $4.64x_0 + 4.12x_2 \geq 26$
- $8.08x_1 + 4.12x_2 \geq 20$
- $4.64x_0 + 8.08x_1 \geq 43$
- $4.64x_0 + 4.12x_2 + 0.75x_3 \geq 27$
- $4.64x_0 + 8.08x_1 + 4.12x_2 + 0.75x_3 \geq 27$
- $5.92x_2 + 4.45x_3 \geq 27$
- $6.34x_0 + 4.69x_1 \geq 39$
- $6.34x_0 + 4.69x_1 + 5.92x_2 + 4.45x_3 \geq 39$
- $x_0 - 9x_2 \geq 0$
- $4.12x_2 + 0.75x_3 \leq 73$
- $4.64x_0 + 8.08x_1 \leq 171$
- $4.64x_0 + 0.75x_3 \leq 92$
- $4.64x_0 + 4.12x_2 + 0.75x_3 \leq 118$
- $5.92x_2 + 4.45x_3 \leq 165$
- $6.34x_0 + 4.45x_3 \leq 86$
- $6.34x_0 + 5.92x_2 \leq 101$
- $6.34x_0 + 4.69x_1 + 4.45x_3 \leq 123$
- $6.34x_0 + 4.69x_1 + 5.92x_2 \leq 129$
- $4.69x_1 + 5.92x_2 + 4.45x_3 \leq 152$

## Step 4: Create the symbolic representation dictionary
```json
{
    'sym_variables': [
        ('x0', 'hours worked by Dale'),
        ('x1', 'hours worked by Jean'),
        ('x2', 'hours worked by Bill'),
        ('x3', 'hours worked by Laura')
    ],
    'objective_function': '7.52*x0 + 5.41*x1 + 2.72*x2 + 9.75*x3',
    'constraints': [
        '4.64*x0 <= 195',
        '6.34*x0 <= 171',
        '8.08*x1 <= 195',
        '4.69*x1 <= 171',
        '4.12*x2 <= 195',
        '5.92*x2 <= 171',
        '0.75*x3 <= 195',
        '4.45*x3 <= 171',
        '4.64*x0 + 4.12*x2 >= 26',
        '8.08*x1 + 4.12*x2 >= 20',
        '4.64*x0 + 8.08*x1 >= 43',
        '4.64*x0 + 4.12*x2 + 0.75*x3 >= 27',
        '4.64*x0 + 8.08*x1 + 4.12*x2 + 0.75*x3 >= 27',
        '5.92*x2 + 4.45*x3 >= 27',
        '6.34*x0 + 4.69*x1 >= 39',
        '6.34*x0 + 4.69*x1 + 5.92*x2 + 4.45*x3 >= 39',
        'x0 - 9*x2 >= 0',
        '4.12*x2 + 0.75*x3 <= 73',
        '4.64*x0 + 8.08*x1 <= 171',
        '4.64*x0 + 0.75*x3 <= 92',
        '4.64*x0 + 4.12*x2 + 0.75*x3 <= 118',
        '5.92*x2 + 4.45*x3 <= 165',
        '6.34*x0 + 4.45*x3 <= 86',
        '6.34*x0 + 5.92*x2 <= 101',
        '6.34*x0 + 4.69*x1 + 4.45*x3 <= 123',
        '6.34*x0 + 4.69*x1 + 5.92*x2 <= 129',
        '4.69*x1 + 5.92*x2 + 4.45*x3 <= 152'
    ]
}
```

## Step 5: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # hours worked by Dale
    x1 = model.addVar(name="x1", lb=0)  # hours worked by Jean
    x2 = model.addVar(name="x2", lb=0)  # hours worked by Bill
    x3 = model.addVar(name="x3", lb=0)  # hours worked by Laura

    # Define the objective function
    model.setObjective(7.52 * x0 + 5.41 * x1 + 2.72 * x2 + 9.75 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(4.64 * x0 <= 195)
    model.addConstr(6.34 * x0 <= 171)
    model.addConstr(8.08 * x1 <= 195)
    model.addConstr(4.69 * x1 <= 171)
    model.addConstr(4.12 * x2 <= 195)
    model.addConstr(5.92 * x2 <= 171)
    model.addConstr(0.75 * x3 <= 195)
    model.addConstr(4.45 * x3 <= 171)
    model.addConstr(4.64 * x0 + 4.12 * x2 >= 26)
    model.addConstr(8.08 * x1 + 4.12 * x2 >= 20)
    model.addConstr(4.64 * x0 + 8.08 * x1 >= 43)
    model.addConstr(4.64 * x0 + 4.12 * x2 + 0.75 * x3 >= 27)
    model.addConstr(4.64 * x0 + 8.08 * x1 + 4.12 * x2 + 0.75 * x3 >= 27)
    model.addConstr(5.92 * x2 + 4.45 * x3 >= 27)
    model.addConstr(6.34 * x0 + 4.69 * x1 >= 39)
    model.addConstr(6.34 * x0 + 4.69 * x1 + 5.92 * x2 + 4.45 * x3 >= 39)
    model.addConstr(x0 - 9 * x2 >= 0)
    model.addConstr(4.12 * x2 + 0.75 * x3 <= 73)
    model.addConstr(4.64 * x0 + 8.08 * x1 <= 171)
    model.addConstr(4.64 * x0 + 0.75 * x3 <= 92)
    model.addConstr(4.64 * x0 + 4.12 * x2 + 0.75 * x3 <= 118)
    model.addConstr(5.92 * x2 + 4.45 * x3 <= 165)
    model.addConstr(6.34 * x0 + 4.45 * x3 <= 86)
    model.addConstr(6.34 * x0 + 5.92 * x2 <= 101)
    model.addConstr(6.34 * x0 + 4.69 * x1 + 4.45 * x3 <= 123)
    model.addConstr(6.34 * x0 + 4.69 * x1 + 5.92 * x2 <= 129)
    model.addConstr(4.69 * x1 + 5.92 * x2 + 4.45 * x3 <= 152)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```