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

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $9.51x_0 + 2.54x_1 + 2.66x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
1. $22x_0 \geq 22x_0$ (redundant, but given)
2. $14x_0 \geq 14x_0$ (redundant, but given)
3. $21x_1 \geq 21x_1$ (redundant, but given)
4. $2x_1 \geq 2x_1$ (redundant, but given)
5. $22x_2 \geq 22x_2$ (redundant, but given)
6. $22x_2 \geq 22x_2$ (redundant, but given)
7. $22x_0 + 21x_1 \geq 26$
8. $22x_0 + 21x_1 + 22x_2 \geq 26$
9. $2x_1 + 22x_2 \geq 16$
10. $14x_0 + 2x_1 \geq 12$
11. $14x_0 + 22x_2 \geq 18$
12. $14x_0 + 2x_1 + 22x_2 \geq 18$
13. $4x_1 - 10x_2 \geq 0$
14. $22x_0 + 21x_1 \leq 138$
15. $22x_0 + 21x_1 + 22x_2 \leq 184$
16. $14x_0 + 2x_1 \leq 59$
17. $2x_1 + 22x_2 \leq 48$
18. $14x_0 + 22x_2 \leq 29$
19. $x_0 \in \mathbb{Z}$
20. $x_1 \in \mathbb{Z}$

## Step 4: Create the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'hours worked by Ringo'),
    ('x1', 'hours worked by Mary'),
    ('x2', 'hours worked by Bobby')
],
'objective_function': '9.51*x0 + 2.54*x1 + 2.66*x2',
'constraints': [
    '22*x0 >= 22*x0',
    '14*x0 >= 14*x0',
    '21*x1 >= 21*x1',
    '2*x1 >= 2*x1',
    '22*x2 >= 22*x2',
    '22*x2 >= 22*x2',
    '22*x0 + 21*x1 >= 26',
    '22*x0 + 21*x1 + 22*x2 >= 26',
    '2*x1 + 22*x2 >= 16',
    '14*x0 + 2*x1 >= 12',
    '14*x0 + 22*x2 >= 18',
    '14*x0 + 2*x1 + 22*x2 >= 18',
    '4*x1 - 10*x2 >= 0',
    '22*x0 + 21*x1 <= 138',
    '22*x0 + 21*x1 + 22*x2 <= 184',
    '14*x0 + 2*x1 <= 59',
    '2*x1 + 22*x2 <= 48',
    '14*x0 + 22*x2 <= 29',
    'x0 % 1 == 0',
    'x1 % 1 == 0'
]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name='x0', vtype=gurobi.GRB.INTEGER)  # hours worked by Ringo
    x1 = model.addVar(name='x1', vtype=gurobi.GRB.INTEGER)  # hours worked by Mary
    x2 = model.addVar(name='x2')  # hours worked by Bobby

    # Set the objective function
    model.setObjective(9.51 * x0 + 2.54 * x1 + 2.66 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(22 * x0 >= 22 * x0)
    model.addConstr(14 * x0 >= 14 * x0)
    model.addConstr(21 * x1 >= 21 * x1)
    model.addConstr(2 * x1 >= 2 * x1)
    model.addConstr(22 * x2 >= 22 * x2)
    model.addConstr(22 * x2 >= 22 * x2)
    model.addConstr(22 * x0 + 21 * x1 >= 26)
    model.addConstr(22 * x0 + 21 * x1 + 22 * x2 >= 26)
    model.addConstr(2 * x1 + 22 * x2 >= 16)
    model.addConstr(14 * x0 + 2 * x1 >= 12)
    model.addConstr(14 * x0 + 22 * x2 >= 18)
    model.addConstr(14 * x0 + 2 * x1 + 22 * x2 >= 18)
    model.addConstr(4 * x1 - 10 * x2 >= 0)
    model.addConstr(22 * x0 + 21 * x1 <= 138)
    model.addConstr(22 * x0 + 21 * x1 + 22 * x2 <= 184)
    model.addConstr(14 * x0 + 2 * x1 <= 59)
    model.addConstr(2 * x1 + 22 * x2 <= 48)
    model.addConstr(14 * x0 + 22 * x2 <= 29)

    # Solve 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)
    else:
        print('No solution found')

solve_optimization_problem()
```