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

## Step 2: Convert the given attributes into a usable format
The given attributes are:
- $r_0$: work quality rating with $x_0 = 16$, $x_1 = 13$, $x_2 = 5$ and an upper bound of 121.
- $r_1$: productivity rating with $x_0 = 11$, $x_1 = 7$, $x_2 = 16$ and an upper bound of 107.
- $r_2$: organization score with $x_0 = 1$, $x_1 = 6$, $x_2 = 7$ and an upper bound of 166.

## 3: Define the objective function in symbolic notation
The objective function to minimize is $1.7x_0 + 9.18x_1 + 6.03x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $16x_0 \leq 121$
- $11x_0 \leq 107$
- $1x_0 \leq 166$
- $13x_1 \leq 121$
- $7x_1 \leq 107$
- $6x_1 \leq 166$
- $5x_2 \leq 121$
- $16x_2 \leq 107$
- $7x_2 \leq 166$
- $16x_0 + 13x_1 \geq 36$
- $13x_1 + 5x_2 \geq 16$
- $16x_0 + 13x_1 + 5x_2 \geq 21$
- $16x_0 + 13x_1 + 5x_2 \geq 21$
- $7x_1 + 16x_2 \geq 19$
- $11x_0 + 16x_2 \geq 21$
- $11x_0 + 7x_1 + 16x_2 \geq 21$
- $1x_0 + 7x_2 \geq 24$
- $1x_0 + 6x_1 \geq 47$
- $6x_1 + 7x_2 \geq 43$
- $1x_0 + 6x_1 + 7x_2 \geq 43$
- $16x_0 + 5x_2 \leq 107$
- $16x_0 + 13x_1 + 5x_2 \leq 108$
- $11x_0 + 16x_2 \leq 40$

## 5: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'hours worked by Mary'), ('x1', 'hours worked by Jean'), ('x2', 'hours worked by George')],
    'objective_function': '1.7*x0 + 9.18*x1 + 6.03*x2',
    'constraints': [
        '16*x0 <= 121', '11*x0 <= 107', '1*x0 <= 166',
        '13*x1 <= 121', '7*x1 <= 107', '6*x1 <= 166',
        '5*x2 <= 121', '16*x2 <= 107', '7*x2 <= 166',
        '16*x0 + 13*x1 >= 36', '13*x1 + 5*x2 >= 16',
        '16*x0 + 13*x1 + 5*x2 >= 21', '16*x0 + 13*x1 + 5*x2 >= 21',
        '7*x1 + 16*x2 >= 19', '11*x0 + 16*x2 >= 21',
        '11*x0 + 7*x1 + 16*x2 >= 21', '1*x0 + 7*x2 >= 24',
        '1*x0 + 6*x1 >= 47', '6*x1 + 7*x2 >= 43',
        '1*x0 + 6*x1 + 7*x2 >= 43', '16*x0 + 5*x2 <= 107',
        '16*x0 + 13*x1 + 5*x2 <= 108', '11*x0 + 16*x2 <= 40'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="hours_worked_by_Mary", lb=0)  # No upper bound for hours worked
x1 = m.addVar(name="hours_worked_by_Jean", lb=0)
x2 = m.addVar(name="hours_worked_by_George", lb=0)

# Define the objective function
m.setObjective(1.7*x0 + 9.18*x1 + 6.03*x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(16*x0 <= 121)
m.addConstr(11*x0 <= 107)
m.addConstr(x0 <= 166)
m.addConstr(13*x1 <= 121)
m.addConstr(7*x1 <= 107)
m.addConstr(6*x1 <= 166)
m.addConstr(5*x2 <= 121)
m.addConstr(16*x2 <= 107)
m.addConstr(7*x2 <= 166)
m.addConstr(16*x0 + 13*x1 >= 36)
m.addConstr(13*x1 + 5*x2 >= 16)
m.addConstr(16*x0 + 13*x1 + 5*x2 >= 21)
m.addConstr(16*x0 + 13*x1 + 5*x2 >= 21)
m.addConstr(7*x1 + 16*x2 >= 19)
m.addConstr(11*x0 + 16*x2 >= 21)
m.addConstr(11*x0 + 7*x1 + 16*x2 >= 21)
m.addConstr(x0 + 7*x2 >= 24)
m.addConstr(x0 + 6*x1 >= 47)
m.addConstr(6*x1 + 7*x2 >= 43)
m.addConstr(x0 + 6*x1 + 7*x2 >= 43)
m.addConstr(16*x0 + 5*x2 <= 107)
m.addConstr(16*x0 + 13*x1 + 5*x2 <= 108)
m.addConstr(11*x0 + 16*x2 <= 40)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Hours worked by Mary: {x0.varValue}")
    print(f"Hours worked by Jean: {x1.varValue}")
    print(f"Hours worked by George: {x2.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```