## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'hours worked by Dale' and 'hours worked by Bill'. Let's denote 'hours worked by Dale' as $x_1$ and 'hours worked by Bill' as $x_2$. The objective function to minimize is $1 \cdot x_1 + 1 \cdot x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $25x_1 + 21x_2 \geq 43$ (total combined work quality rating)
- $20x_1 + 1x_2 \geq 36$ (total combined organization score)
- $11x_1 + 27x_2 \geq 47$ (total combined paperwork competence rating)
- $-8x_1 + 2x_2 \geq 0$
- $25x_1 + 21x_2 \leq 80$ (total combined work quality rating upper bound)
- $20x_1 + 1x_2 \leq 105$ (total combined organization score upper bound)
- $11x_1 + 27x_2 \leq 65$ (total combined paperwork competence rating upper bound)

## 3: Define the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'hours worked by Dale'), ('x2', 'hours worked by Bill')],
'objective_function': 'x1 + x2',
'constraints': [
    '25*x1 + 21*x2 >= 43',
    '20*x1 + x2 >= 36',
    '11*x1 + 27*x2 >= 47',
    '-8*x1 + 2*x2 >= 0',
    '25*x1 + 21*x2 <= 80',
    '20*x1 + x2 <= 105',
    '11*x1 + 27*x2 <= 65'
]
}
```

## 4: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='hours_worked_by_Dale', lb=0)  # Assuming hours cannot be negative
    x2 = model.addVar(name='hours_worked_by_Bill', lb=0)  # Assuming hours cannot be negative

    # Define the objective function
    model.setObjective(x1 + x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(25*x1 + 21*x2 >= 43, name='work_quality_rating')
    model.addConstr(20*x1 + x2 >= 36, name='organization_score')
    model.addConstr(11*x1 + 27*x2 >= 47, name='paperwork_competence_rating')
    model.addConstr(-8*x1 + 2*x2 >= 0, name='hourly_constraint')
    model.addConstr(25*x1 + 21*x2 <= 80, name='work_quality_upper_bound')
    model.addConstr(20*x1 + x2 <= 105, name='organization_upper_bound')
    model.addConstr(11*x1 + 27*x2 <= 65, name='paperwork_upper_bound')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Hours worked by Dale: {x1.varValue}')
        print(f'Hours worked by Bill: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```