## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by George', 'hours worked by John', and 'hours worked by Dale'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic variables
The symbolic representation of the variables is:
- $x_1$ for 'hours worked by George'
- $x_2$ for 'hours worked by John'
- $x_3$ for 'hours worked by Dale'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $4x_1 + 5x_2 + 8x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $4x_1 \leq 104$ (George and John's combined organization score)
- $4x_1 + 10x_2 \leq 104$ is incorrect based on the problem statement. The correct constraints are:
- $4x_1 + 10x_2 \leq 104$ (George and John's combined organization score)
- $4x_1 + 10x_2 + 4x_3 \leq 160$ (Total combined organization score)
- $x_1$ is an integer
- $x_2$ is a continuous variable
- $x_3$ is an integer

## 5: Correct the constraints based on the problem description
The correct constraints should be interpreted as:
- The organization score for George is 4, for John is 10, and for Dale is 4.
- The total combined organization score from hours worked by George and John is at most 104: $4x_1 + 10x_2 \leq 104$
- The total combined organization score from hours worked by George, John, and Dale is at most 160: $4x_1 + 10x_2 + 4x_3 \leq 160$

## 6: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'hours worked by George'), ('x2', 'hours worked by John'), ('x3', 'hours worked by Dale')],
    'objective_function': '4*x1 + 5*x2 + 8*x3',
    'constraints': [
        '4*x1 + 10*x2 <= 104',
        '4*x1 + 10*x2 + 4*x3 <= 160',
        'x1 >= 0',
        'x2 >= 0',
        'x3 >= 0'
    ],
    'integrality_constraints': [
        'x1 is an integer',
        'x3 is an integer'
    ]
}
```

## 7: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='x1', vtype=gurobi.GRB.INTEGER)  # hours worked by George
    x2 = model.addVar(name='x2', vtype=gurobi.GRB.CONTINUOUS)  # hours worked by John
    x3 = model.addVar(name='x3', vtype=gurobi.GRB.INTEGER)  # hours worked by Dale

    # Define the objective function
    model.setObjective(4 * x1 + 5 * x2 + 8 * x3, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(4 * x1 + 10 * x2 <= 104)
    model.addConstr(4 * x1 + 10 * x2 + 4 * x3 <= 160)
    model.addConstr(x1 >= 0)
    model.addConstr(x2 >= 0)
    model.addConstr(x3 >= 0)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('Hours worked by George: ', x1.varValue)
        print('Hours worked by John: ', x2.varValue)
        print('Hours worked by Dale: ', x3.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```