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

Let's denote:
- $x_1$ as 'hours worked by Paul',
- $x_2$ as 'hours worked by Ringo', and
- $x_3$ as 'hours worked by Bobby'.

The objective function to minimize is: $1.09x_1 + 1.75x_2 + 1.36x_3$.

Constraints:
1. Organization score constraints for each individual are not directly applicable as constraints but inform the coefficients in other constraints.
2. The total combined organization score from hours worked by Paul and Bobby should be at least 16: $8x_1 + 13x_3 \geq 16$.
3. The total combined organization score from hours worked by Paul and Ringo should be at least 9: $8x_1 + 18x_2 \geq 9$.
4. The total combined organization score from all should be at least 9: $8x_1 + 18x_2 + 13x_3 \geq 9$.
5. $-8x_1 + 7x_3 \geq 0$.
6. $-8x_1 + 4x_2 \geq 0$.
7. The total combined organization score from Ringo and Bobby should be 32 or less: $18x_2 + 13x_3 \leq 32$.
8. The total combined organization score from Paul and Bobby should be 52 or less: $8x_1 + 13x_3 \leq 52$.

Given these definitions, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'hours worked by Paul'), ('x2', 'hours worked by Ringo'), ('x3', 'hours worked by Bobby')],
    'objective_function': '1.09*x1 + 1.75*x2 + 1.36*x3',
    'constraints': [
        '8*x1 + 13*x3 >= 16',
        '8*x1 + 18*x2 >= 9',
        '8*x1 + 18*x2 + 13*x3 >= 9',
        '-8*x1 + 7*x3 >= 0',
        '-8*x1 + 4*x2 >= 0',
        '18*x2 + 13*x3 <= 32',
        '8*x1 + 13*x3 <= 52'
    ]
}
```

Now, let's implement this optimization problem using Gurobi in Python. We need to ensure that `x1` can be fractional, `x2` is an integer, and `x3` can also be fractional.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Paul")
x2 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="hours_worked_by_Ringo")
x3 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Bobby")

# Set the objective function
m.setObjective(1.09*x1 + 1.75*x2 + 1.36*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(8*x1 + 13*x3 >= 16, name="Paul_and_Bobby_min_score")
m.addConstr(8*x1 + 18*x2 >= 9, name="Paul_and_Ringo_min_score")
m.addConstr(8*x1 + 18*x2 + 13*x3 >= 9, name="All_min_score")
m.addConstr(-8*x1 + 7*x3 >= 0, name="Paul_and_Bobby_balance")
m.addConstr(-8*x1 + 4*x2 >= 0, name="Paul_and_Ringo_balance")
m.addConstr(18*x2 + 13*x3 <= 32, name="Ringo_and_Bobby_max_score")
m.addConstr(8*x1 + 13*x3 <= 52, name="Paul_and_Bobby_max_score")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    for var in m.getVars():
        print(f"{var.varName}: {var.x}")
else:
    print("No optimal solution found")
```