To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using algebraic terms.

Let's denote:
- $x_0$ as the hours worked by Mary,
- $x_1$ as the hours worked by Paul,
- $x_2$ as the hours worked by Bobby.

The objective function to minimize is: $1.38x_0 + 7.91x_1 + 2.21x_2$.

The constraints are:
1. Organization score for Mary: $x_0$ has a score of 1.
2. Organization score for Paul: $x_1$ has a score of 8.
3. Organization score for Bobby: $x_2$ has a score of 11.
4. Combined organization score from hours worked by Mary and Bobby: $1x_0 + 11x_2 \geq 42$.
5. Combined organization score from hours worked by Mary and Paul: $1x_0 + 8x_1 \geq 14$.
6. Total combined organization score from hours worked by all: $1x_0 + 8x_1 + 11x_2 \geq 25$.
7. Another representation of the total combined organization score: $1x_0 + 8x_1 + 11x_2 \geq 25$ (same as constraint 6).
8. Constraint on hours worked by Mary and Bobby: $-5x_0 + 5x_2 \geq 0$.
9. Upper bound on the combined organization score from Paul and Bobby: $8x_1 + 11x_2 \leq 80$.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x0', 'hours worked by Mary'), ('x1', 'hours worked by Paul'), ('x2', 'hours worked by Bobby')],
    'objective_function': '1.38*x0 + 7.91*x1 + 2.21*x2',
    'constraints': [
        '1*x0 + 11*x2 >= 42',
        '1*x0 + 8*x1 >= 14',
        '1*x0 + 8*x1 + 11*x2 >= 25',
        '1*x0 + 8*x1 + 11*x2 >= 25',
        '-5*x0 + 5*x2 >= 0',
        '8*x1 + 11*x2 <= 80'
    ]
}
```

Now, let's implement this problem in Gurobi Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define the variables
x0 = m.addVar(lb=0, name="hours_worked_by_Mary")
x1 = m.addVar(vtype=GRB.INTEGER, lb=0, name="hours_worked_by_Paul")
x2 = m.addVar(lb=0, name="hours_worked_by_Bobby")

# Define the objective function
m.setObjective(1.38*x0 + 7.91*x1 + 2.21*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(1*x0 + 11*x2 >= 42)
m.addConstr(1*x0 + 8*x1 >= 14)
m.addConstr(1*x0 + 8*x1 + 11*x2 >= 25)
m.addConstr(-5*x0 + 5*x2 >= 0)
m.addConstr(8*x1 + 11*x2 <= 80)

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Mary: {x0.x}")
    print(f"Hours worked by Paul: {x1.x}")
    print(f"Hours worked by Bobby: {x2.x}")
else:
    print("No optimal solution found")
```