To solve the given optimization problem, we first need to understand and translate the natural language description into a symbolic representation. The variables are 'hours worked by Peggy', 'hours worked by Ringo', and 'hours worked by Laura'. We will denote these as $x_1$, $x_2$, and $x_3$ respectively.

The objective function is to maximize $6.32x_1 + 6.84x_2 + 4.46x_3$.

The constraints are:
1. The organization score for Peggy, Ringo, and Laura are 1, 12, and 3 respectively.
2. The total combined organization score from hours worked by Peggy and Ringo should be at most 60.
3. The total combined organization score from hours worked by Peggy and Laura must be at most 147.
4. The total combined organization score from hours worked by Peggy, Ringo, and Laura must be as much or less than 179 (since the upper bound for 'r0' is given as 179 in the context).

Symbolically representing these constraints with $x_1$, $x_2$, and $x_3$:
- $x_1 + 12x_2 \leq 60$
- $x_1 + 3x_3 \leq 147$
- $x_1 + 12x_2 + 3x_3 \leq 179$

Given this understanding, the symbolic representation of the problem can be summarized as:

```json
{
    'sym_variables': [('x1', 'hours worked by Peggy'), ('x2', 'hours worked by Ringo'), ('x3', 'hours worked by Laura')],
    'objective_function': '6.32*x1 + 6.84*x2 + 4.46*x3',
    'constraints': ['x1 + 12*x2 <= 60', 'x1 + 3*x3 <= 147', 'x1 + 12*x2 + 3*x3 <= 179']
}
```

To solve this problem using Gurobi, we can use the following Python code:

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(name="hours_worked_by_Peggy", lb=0)
x2 = m.addVar(name="hours_worked_by_Ringo", lb=0)
x3 = m.addVar(name="hours_worked_by_Laura", lb=0)

# Set the objective function
m.setObjective(6.32*x1 + 6.84*x2 + 4.46*x3, GRB.MAXIMIZE)

# Add constraints to the model
m.addConstr(x1 + 12*x2 <= 60)
m.addConstr(x1 + 3*x3 <= 147)
m.addConstr(x1 + 12*x2 + 3*x3 <= 179)

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Objective Value:", m.objVal)
```