To solve this optimization problem, we first need to understand the problem description, including the objective function and the constraints. The objective function to minimize is given as:

\[ 3.39 \times D^2 + 4.91 \times D \times H + 6.53 \times D \times G + 7.29 \times D \times B + 8.13 \times D \times L + 7.6 \times H^2 + 9.61 \times H \times G + 6.55 \times H \times L + 9.68 \times G^2 + 4.11 \times G \times B + 9.08 \times G \times L + 9.4 \times L^2 + 2.71 \times D + 7.89 \times G + 9.21 \times B + 5.64 \times L \]

where \(D\), \(H\), \(G\), \(B\), and \(L\) represent the hours worked by Dale, Hank, George, Bobby, and Laura, respectively.

The constraints include bounds on computer competence ratings and dollar costs per hour for each individual, as well as combined constraints across individuals.

Here is the Gurobi code to model and solve this problem:

```python
import gurobi

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

    # Define variables
    D = model.addVar(name="Dale", lb=0, ub=None, vtype=gurobi.GRB.INTEGER)  # Integer
    H = model.addVar(name="Hank", lb=0, ub=None)  # Continuous
    G = model.addVar(name="George", lb=0, ub=None, vtype=gurobi.GRB.INTEGER)  # Integer
    B = model.addVar(name="Bobby", lb=0, ub=None, vtype=gurobi.GRB.INTEGER)  # Integer
    L = model.addVar(name="Laura", lb=0, ub=None, vtype=gurobi.GRB.INTEGER)  # Integer

    # Objective function
    model.setObjective(3.39 * D**2 + 4.91 * D * H + 6.53 * D * G + 7.29 * D * B + 8.13 * D * L +
                       7.6 * H**2 + 9.61 * H * G + 6.55 * H * L + 9.68 * G**2 + 4.11 * G * B + 9.08 * G * L +
                       9.4 * L**2 + 2.71 * D + 7.89 * G + 9.21 * B + 5.64 * L, gurobi.GRB.MINIMIZE)

    # Constraints
    # Individual computer competence ratings
    model.addConstr(D <= 153, name="D_computer_rating")
    model.addConstr(6 * H <= 153, name="H_computer_rating")
    model.addConstr(12 * G <= 153, name="G_computer_rating")
    model.addConstr(B <= 153, name="B_computer_rating")
    model.addConstr(L <= 153, name="L_computer_rating")

    # Individual dollar cost per hour
    model.addConstr(12 * D <= 243, name="D_dollar_cost")
    model.addConstr(10 * H <= 243, name="H_dollar_cost")
    model.addConstr(2 * G <= 243, name="G_dollar_cost")
    model.addConstr(12 * B <= 243, name="B_dollar_cost")
    model.addConstr(7 * L <= 243, name="L_dollar_cost")

    # Combined computer competence ratings
    model.addConstr(B + L >= 18, name="B_L_computer_rating")
    model.addConstr(D**2 + G**2 >= 14, name="D_G_computer_rating")
    model.addConstr(H + L >= 23, name="H_L_computer_rating")
    model.addConstr(H + B >= 24, name="H_B_computer_rating")
    model.addConstr(D + B >= 18, name="D_B_computer_rating")
    model.addConstr(D + H + G + B + L >= 18, name="all_computer_rating")

    # Combined dollar costs
    model.addConstr(D**2 + H**2 >= 39, name="D_H_dollar_cost")
    model.addConstr(D * G >= 45, name="D_G_dollar_cost")
    model.addConstr(D + B >= 30, name="D_B_dollar_cost")
    model.addConstr(D**2 + H**2 + G**2 >= 37, name="D_H_G_dollar_cost")
    model.addConstr(D * H * B >= 37, name="D_H_B_dollar_cost")  # Note: This might need adjustment as it's nonlinear
    model.addConstr(H * B * L >= 37, name="H_B_L_dollar_cost")
    model.addConstr(D**2 + H**2 + G**2 >= 33, name="D_H_G_dollar_cost_33")
    model.addConstr(D + H + B >= 33, name="D_H_B_dollar_cost_33")
    model.addConstr(H + B + L >= 33, name="H_B_L_dollar_cost_33")

    # More combined dollar costs
    model.addConstr(D * H * G >= 41, name="D_H_G_dollar_cost_41")
    model.addConstr(D * H * B >= 41, name="D_H_B_dollar_cost_41")
    model.addConstr(H * B * L >= 41, name="H_B_L_dollar_cost_41")
    model.addConstr(D * H * G * B * L >= 41, name="all_dollar_cost")

    # Linear constraints
    model.addConstr(4 * D - 7 * G >= 0, name="linear_constraint_1")
    model.addConstr(-9 * G + 8 * B >= 0, name="linear_constraint_2")

    # Upper bounds for computer competence ratings
    model.addConstr(B + L <= 107, name="B_L_computer_rating_max")
    model.addConstr(D**2 + H**2 + G**2 <= 135, name="D_H_G_computer_rating_max")
    model.addConstr(D**2 + H**2 + L**2 <= 83, name="D_H_L_computer_rating_max")
    model.addConstr(G + B + L <= 52, name="G_B_L_computer_rating_max")
    model.addConstr(D + B + L <= 129, name="D_B_L_computer_rating_max")
    model.addConstr(D + G + B <= 120, name="D_G_B_computer_rating_max")
    model.addConstr(H + G + L <= 113, name="H_G_L_computer_rating_max")
    model.addConstr(H + G + B <= 129, name="H_G_B_computer_rating_max")
    model.addConstr(D + G + L <= 78, name="D_G_L_computer_rating_max")
    model.addConstr(H + B + L <= 94, name="H_B_L_computer_rating_max")

    # Upper bounds for dollar costs
    model.addConstr(D**2 + H**2 + L**2 <= 106, name="D_H_L_dollar_cost_max")
    model.addConstr(D + H + G <= 189, name="D_H_G_dollar_cost_max")
    model.addConstr(D * G * B <= 93, name="D_G_B_dollar_cost_max")  # Note: Nonlinear
    model.addConstr(H**2 + G**2 + B**2 <= 148, name="H_G_B_dollar_cost_max")
    model.addConstr(H + B + L <= 234, name="H_B_L_dollar_cost_max")
    model.addConstr(D**2 + G**2 + L**2 <= 202, name="D_G_L_dollar_cost_max")
    model.addConstr(G + B + L <= 135, name="G_B_L_dollar_cost_max")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Dale: ", D.varValue)
        print("Hank: ", H.varValue)
        print("George: ", G.varValue)
        print("Bobby: ", B.varValue)
        print("Laura: ", L.varValue)
    else:
        print("No solution found")

optimize_problem()
```