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

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $1.52x_0 + 6.32x_1 + 9.17x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $5x_0 \leq 244$
- $9x_0 \leq 659$
- $21x_0 \leq 331$
- $6x_1 \leq 244$
- $13x_1 \leq 659$
- $4x_1 \leq 331$
- $35x_2 \leq 244$
- $13x_2 \leq 659$
- $5x_2 \leq 331$
- $6x_1 + 35x_2 \geq 56$
- $5x_0 + 6x_1 \geq 31$
- $5x_0 + 6x_1 + 35x_2 \geq 31$
- $13x_1 + 13x_2 \geq 208$
- $9x_0 + 13x_2 \geq 198$
- $9x_0 + 13x_1 + 13x_2 \geq 198$
- $21x_0 + 4x_1 \geq 48$
- $21x_0 + 4x_1 + 5x_2 \geq 48$
- $-5x_0 + 6x_2 \geq 0$
- $4x_0 - 5x_1 \geq 0$
- $6x_1 + 35x_2 \leq 186$
- $5x_0 + 6x_1 \leq 84$
- $21x_0 + 5x_2 \leq 126$
- $21x_0 + 4x_1 \leq 202$
- $21x_0 + 4x_1 + 5x_2 \leq 232$
- $x_0 \geq 0$ (implicit, as hours cannot be negative)
- $x_1 \geq 0$ (implicit, as hours cannot be negative)
- $x_2$ is an integer

## Step 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'hours worked by Ringo'), ('x1', 'hours worked by George'), ('x2', 'hours worked by Jean')],
    'objective_function': '1.52*x0 + 6.32*x1 + 9.17*x2',
    'constraints': [
        '5*x0 <= 244',
        '9*x0 <= 659',
        '21*x0 <= 331',
        '6*x1 <= 244',
        '13*x1 <= 659',
        '4*x1 <= 331',
        '35*x2 <= 244',
        '13*x2 <= 659',
        '5*x2 <= 331',
        '6*x1 + 35*x2 >= 56',
        '5*x0 + 6*x1 >= 31',
        '5*x0 + 6*x1 + 35*x2 >= 31',
        '13*x1 + 13*x2 >= 208',
        '9*x0 + 13*x2 >= 198',
        '9*x0 + 13*x1 + 13*x2 >= 198',
        '21*x0 + 4*x1 >= 48',
        '21*x0 + 4*x1 + 5*x2 >= 48',
        '-5*x0 + 6*x2 >= 0',
        '4*x0 - 5*x1 >= 0',
        '6*x1 + 35*x2 <= 186',
        '5*x0 + 6*x1 <= 84',
        '21*x0 + 5*x2 <= 126',
        '21*x0 + 4*x1 <= 202',
        '21*x0 + 4*x1 + 5*x2 <= 232',
        'x0 >= 0',
        'x1 >= 0'
    ]
}
```

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

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

# Define the variables
x0 = m.addVar(name="x0", lb=0, vtype=gurobi.GRB.CONTINUOUS)  # hours worked by Ringo
x1 = m.addVar(name="x1", lb=0, vtype=gurobi.GRB.CONTINUOUS)  # hours worked by George
x2 = m.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # hours worked by Jean

# Define the objective function
m.setObjective(1.52*x0 + 6.32*x1 + 9.17*x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x0 <= 244)
m.addConstr(9*x0 <= 659)
m.addConstr(21*x0 <= 331)
m.addConstr(6*x1 <= 244)
m.addConstr(13*x1 <= 659)
m.addConstr(4*x1 <= 331)
m.addConstr(35*x2 <= 244)
m.addConstr(13*x2 <= 659)
m.addConstr(5*x2 <= 331)
m.addConstr(6*x1 + 35*x2 >= 56)
m.addConstr(5*x0 + 6*x1 >= 31)
m.addConstr(5*x0 + 6*x1 + 35*x2 >= 31)
m.addConstr(13*x1 + 13*x2 >= 208)
m.addConstr(9*x0 + 13*x2 >= 198)
m.addConstr(9*x0 + 13*x1 + 13*x2 >= 198)
m.addConstr(21*x0 + 4*x1 >= 48)
m.addConstr(21*x0 + 4*x1 + 5*x2 >= 48)
m.addConstr(-5*x0 + 6*x2 >= 0)
m.addConstr(4*x0 - 5*x1 >= 0)
m.addConstr(6*x1 + 35*x2 <= 186)
m.addConstr(5*x0 + 6*x1 <= 84)
m.addConstr(21*x0 + 5*x2 <= 126)
m.addConstr(21*x0 + 4*x1 <= 202)
m.addConstr(21*x0 + 4*x1 + 5*x2 <= 232)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Hours worked by Ringo: ", x0.varValue)
    print("Hours worked by George: ", x1.varValue)
    print("Hours worked by Jean: ", x2.varValue)
else:
    print("The model is infeasible")
```