To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's denote:
- $x_0$ as the hours worked by Hank,
- $x_1$ as the hours worked by Jean.

The objective function is to minimize: $8.95x_0 + 2.52x_1$

Constraints are:
1. Organization score constraint: $21x_0 + 13x_1 \geq 69$
2. Likelihood to quit index constraint: $30x_0 + 4x_1 \geq 111$
3. Paperwork competence rating constraint: $27x_0 + 28x_1 \geq 51$
4. Linear combination constraint: $-5x_0 + 4x_1 \geq 0$
5. Upper bound on organization score: $21x_0 + 13x_1 \leq 125$
6. Upper bound on likelihood to quit index: $30x_0 + 4x_1 \leq 274$
7. Upper bound on paperwork competence rating: $27x_0 + 28x_1 \leq 168$

Given the nature of the variables, $x_0$ can be non-integer (hours worked by Hank), but $x_1$ must be an integer (hours worked by Jean).

Here is the symbolic representation in JSON format:
```json
{
  'sym_variables': [('x0', 'hours worked by Hank'), ('x1', 'hours worked by Jean')],
  'objective_function': '8.95*x0 + 2.52*x1',
  'constraints': [
    '21*x0 + 13*x1 >= 69',
    '30*x0 + 4*x1 >= 111',
    '27*x0 + 28*x1 >= 51',
    '-5*x0 + 4*x1 >= 0',
    '21*x0 + 13*x1 <= 125',
    '30*x0 + 4*x1 <= 274',
    '27*x0 + 28*x1 <= 168'
  ]
}
```

Now, let's implement this problem in Gurobi Python:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Hank")
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="hours_worked_by_Jean")

# Objective function
m.setObjective(8.95*x0 + 2.52*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(21*x0 + 13*x1 >= 69, "organization_score_constraint")
m.addConstr(30*x0 + 4*x1 >= 111, "likelihood_to_quit_index_constraint")
m.addConstr(27*x0 + 28*x1 >= 51, "paperwork_competence_rating_constraint")
m.addConstr(-5*x0 + 4*x1 >= 0, "linear_combination_constraint")
m.addConstr(21*x0 + 13*x1 <= 125, "upper_bound_organization_score")
m.addConstr(30*x0 + 4*x1 <= 274, "upper_bound_likelihood_to_quit_index")
m.addConstr(27*x0 + 28*x1 <= 168, "upper_bound_paperwork_competence_rating")

# Optimize
m.optimize()

if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Hank: {x0.x}")
    print(f"Hours worked by Jean: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```