## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Bobby' and 'hours worked by Peggy', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $2.56x_1 + 4.52x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $16x_1 \geq 16$ is not correct, $x_1$ and $x_2$ are hours worked, not multipliers of scores, so we have:
- $16 \leq 16$ (constant, not a constraint on $x_1$ or $x_2$)
- $18 \leq 18$ (constant, not a constraint on $x_1$ or $x_2$)
- $17 \leq 17$ (constant, not a constraint on $x_1$ or $x_2$)
- $23 \leq 23$ (constant, not a constraint on $x_1$ or $x_2$)
- $16x_1 + 17x_2 \geq 25$
- $18x_1 + 23x_2 \geq 51$
- $-8x_1 + 6x_2 \geq 0$
- $16x_1 + 17x_2 \leq 147$
- $18x_1 + 23x_2 \leq 103$

## 4: Correctly interpret the constraints
Given that $x_0 = 16$, $x_1 = 17$, $r0_{upper\_bound} = 150$, and similarly for $r1$, we see that:
- The organization score and computer competence rating for Bobby and Peggy are fixed and not directly constraints on $x_1$ and $x_2$ but rather $r0$ and $r1$ have upper bounds.

## 5: Symbolic Representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'hours worked by Bobby'), ('x2', 'hours worked by Peggy')],
'objective_function': '2.56*x1 + 4.52*x2',
'constraints': [
    '16*x1 + 17*x2 >= 25',
    '18*x1 + 23*x2 >= 51',
    '-8*x1 + 6*x2 >= 0',
    '16*x1 + 17*x2 <= 147',
    '18*x1 + 23*x2 <= 103'
]
}
```

## 6: Gurobi Code
Now, let's write the Gurobi code for this problem:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="hours_worked_by_Bobby", lb=0)  # Assuming hours cannot be negative
    x2 = model.addVar(name="hours_worked_by_Peggy", lb=0)  # Assuming hours cannot be negative

    # Define objective function
    model.setObjective(2.56 * x1 + 4.52 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(16 * x1 + 17 * x2 >= 25, name="org_score_min")
    model.addConstr(18 * x1 + 23 * x2 >= 51, name="comp_comp_min")
    model.addConstr(-8 * x1 + 6 * x2 >= 0, name="mix_constraint")
    model.addConstr(16 * x1 + 17 * x2 <= 147, name="org_score_max")
    model.addConstr(18 * x1 + 23 * x2 <= 103, name="comp_comp_max")

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```