## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Hank', 'hours worked by Bill', and 'hours worked by George', which can be represented as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $2.14x_0 + 7.59x_1 + 8.12x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $13x_0 \geq 61$ (total combined dollar cost per hour from hours worked by Hank, Bill, and George)
- $23x_0 + 6x_1 \geq 27$ (total combined work quality rating from hours worked by Hank and Bill)
- $23x_0 + 6x_1 + 7x_2 \geq 31$ (total combined work quality rating from hours worked by Hank, Bill, and George)
- $19x_1 + 13x_2 \leq 91$ (total combined computer competence rating from hours worked by Bill and George)
- $14x_0 + 19x_1 \leq 136$ (total combined computer competence rating from hours worked by Hank and Bill)
- $14x_0 + 19x_1 + 13x_2 \leq 136$ (total combined computer competence rating from hours worked by Hank, Bill, and George)
- $10x_1 + 5x_2 \leq 133$ (total combined dollar cost per hour from hours worked by Bill and George)
- $13x_0 + 5x_2 \leq 169$ (total combined dollar cost per hour from hours worked by Hank and George)
- $13x_0 + 10x_1 \leq 81$ (total combined dollar cost per hour from hours worked by Hank and Bill)
- $13x_0 + 10x_1 + 5x_2 \leq 173$ (total combined dollar cost per hour from hours worked by Hank, Bill, and George)
- $6x_1 + 7x_2 \leq 90$ (total combined work quality rating from hours worked by Bill and George)
- $23x_0 + 6x_1 \leq 177$ (total combined work quality rating from hours worked by Hank and Bill)
- $23x_0 + 6x_1 + 7x_2 \leq 177$ (total combined work quality rating from hours worked by Hank, Bill, and George)
- $7x_0 + 16x_2 \leq 98$ (total combined organization score from hours worked by Hank and George)
- $7x_0 + 14x_1 + 16x_2 \leq 98$ (total combined organization score from hours worked by Hank, Bill, and George)
- $x_0$ is an integer (whole number amount of hours worked by Hank)
- $x_1$ is a float (non-integer amount of hours worked by Bill)
- $x_2$ is a float (non-integer amount of hours worked by George)

## Step 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'hours worked by Hank'),
        ('x1', 'hours worked by Bill'),
        ('x2', 'hours worked by George')
    ],
    'objective_function': '2.14*x0 + 7.59*x1 + 8.12*x2',
    'constraints': [
        '13*x0 + 10*x1 + 5*x2 >= 61',
        '23*x0 + 6*x1 >= 27',
        '23*x0 + 6*x1 + 7*x2 >= 31',
        '19*x1 + 13*x2 <= 91',
        '14*x0 + 19*x1 <= 136',
        '14*x0 + 19*x1 + 13*x2 <= 136',
        '10*x1 + 5*x2 <= 133',
        '13*x0 + 5*x2 <= 169',
        '13*x0 + 10*x1 <= 81',
        '13*x0 + 10*x1 + 5*x2 <= 173',
        '6*x1 + 7*x2 <= 90',
        '23*x0 + 6*x1 <= 177',
        '23*x0 + 6*x1 + 7*x2 <= 177',
        '7*x0 + 16*x2 <= 98',
        '7*x0 + 14*x1 + 16*x2 <= 98'
    ]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name='x0', vtype='I')  # integer
x1 = model.addVar(name='x1')  # continuous
x2 = model.addVar(name='x2')  # continuous

# Set the objective function
model.setObjective(2.14 * x0 + 7.59 * x1 + 8.12 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(13 * x0 + 10 * x1 + 5 * x2 >= 61)
model.addConstr(23 * x0 + 6 * x1 >= 27)
model.addConstr(23 * x0 + 6 * x1 + 7 * x2 >= 31)
model.addConstr(19 * x1 + 13 * x2 <= 91)
model.addConstr(14 * x0 + 19 * x1 <= 136)
model.addConstr(14 * x0 + 19 * x1 + 13 * x2 <= 136)
model.addConstr(10 * x1 + 5 * x2 <= 133)
model.addConstr(13 * x0 + 5 * x2 <= 169)
model.addConstr(13 * x0 + 10 * x1 <= 81)
model.addConstr(13 * x0 + 10 * x1 + 5 * x2 <= 173)
model.addConstr(6 * x1 + 7 * x2 <= 90)
model.addConstr(23 * x0 + 6 * x1 <= 177)
model.addConstr(23 * x0 + 6 * x1 + 7 * x2 <= 177)
model.addConstr(7 * x0 + 16 * x2 <= 98)
model.addConstr(7 * x0 + 14 * x1 + 16 * x2 <= 98)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objval)
    print('x0: ', x0.varValue)
    print('x1: ', x1.varValue)
    print('x2: ', x2.varValue)
else:
    print('The model is infeasible')
```