## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Bobby', 'hours worked by Peggy', 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 attributes given are:
- $r_0$: {'description': 'dollar cost per hour', 'upper_bound': 299, 'x0': 13, 'x1': 10, 'x2': 26}
- $r_1$: {'description': 'paperwork competence rating', 'upper_bound': 355, 'x0': 8, 'x1': 9, 'x2': 6}

## 3: Define the objective function in symbolic notation
The objective function to minimize is $5x_0 + 8x_1 + 8x_2$.

## 4: List all constraints in symbolic notation
1. $10x_1 + 26x_2 \geq 99$
2. $13x_0 + 10x_1 \geq 97$
3. $13x_0 + 10x_1 + 26x_2 \geq 64$
4. $8x_0 + 9x_1 + 6x_2 \geq 68$
5. $8x_0 + 9x_1 \geq 86$
6. $8x_0 + 6x_2 \geq 41$
7. $8x_0 + 9x_1 + 6x_2 \geq 41$
8. $-6x_0 + 4x_1 \geq 0$
9. $-7x_1 + 6x_2 \geq 0$
10. $8x_0 + 6x_2 \leq 272$
11. $9x_1 + 6x_2 \leq 191$
12. $x_0$ is an integer
13. $x_1$ is an integer
14. $x_2$ is an integer

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'hours worked by Bobby'), ('x1', 'hours worked by Peggy'), ('x2', 'hours worked by George')],
'objective_function': '5*x0 + 8*x1 + 8*x2',
'constraints': [
'10*x1 + 26*x2 >= 99',
'13*x0 + 10*x1 >= 97',
'13*x0 + 10*x1 + 26*x2 >= 64',
'8*x0 + 9*x1 + 6*x2 >= 68',
'8*x0 + 9*x1 >= 86',
'8*x0 + 6*x2 >= 41',
'8*x0 + 9*x1 + 6*x2 >= 41',
'-6*x0 + 4*x1 >= 0',
'-7*x1 + 6*x2 >= 0',
'8*x0 + 6*x2 <= 272',
'9*x1 + 6*x2 <= 191',
'x0 >= 0', 'x1 >= 0', 'x2 >= 0',
'x0 == int(x0)', 'x1 == int(x1)', 'x2 == int(x2)'
]
}
```

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

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(name="x0", vtype=gp.GRB.INTEGER)  # hours worked by Bobby
x1 = m.addVar(name="x1", vtype=gp.GRB.INTEGER)  # hours worked by Peggy
x2 = m.addVar(name="x2", vtype=gp.GRB.INTEGER)  # hours worked by George

# Define the objective function
m.setObjective(5 * x0 + 8 * x1 + 8 * x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(10 * x1 + 26 * x2 >= 99)
m.addConstr(13 * x0 + 10 * x1 >= 97)
m.addConstr(13 * x0 + 10 * x1 + 26 * x2 >= 64)
m.addConstr(8 * x0 + 9 * x1 + 6 * x2 >= 68)
m.addConstr(8 * x0 + 9 * x1 >= 86)
m.addConstr(8 * x0 + 6 * x2 >= 41)
m.addConstr(8 * x0 + 9 * x1 + 6 * x2 >= 41)
m.addConstr(-6 * x0 + 4 * x1 >= 0)
m.addConstr(-7 * x1 + 6 * x2 >= 0)
m.addConstr(8 * x0 + 6 * x2 <= 272)
m.addConstr(9 * x1 + 6 * x2 <= 191)

# Optimize the model
m.optimize()

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