To address the problem described, we'll break down the task into two main parts: 

1. **Symbolic Representation**: Providing a symbolic representation of the optimization problem.
2. **Gurobi Code Implementation**: Implementing the problem in Gurobi to find an optimal solution.

### Symbolic Representation
Given:
- Variables: `x0` for mechanical pencils, `x1` for headsets.
- Objective Function: Minimize `1.36*x0 + 8.25*x1`.
- Constraints:
  - Employee satisfaction impact: `9*x0 + 8*x1 >= 44` and `9*x0 + 8*x1 <= 48`.
  - Usefulness rating: `5*x0 + 8*x1 >= 11` and `5*x0 + 8*x1 <= 25`.
  - Storage space: `2*x0 + 2*x1 >= 39` and `2*x0 + 2*x1 <= 71`.
  - Additional constraint: `3*x0 - 3*x1 >= 0`.

Symbolic representation:
```json
{
  'sym_variables': [('x0', 'mechanical pencils'), ('x1', 'headsets')],
  'objective_function': 'Minimize 1.36*x0 + 8.25*x1',
  'constraints': [
    '9*x0 + 8*x1 >= 44',
    '9*x0 + 8*x1 <= 48',
    '5*x0 + 8*x1 >= 11',
    '5*x0 + 8*x1 <= 25',
    '2*x0 + 2*x1 >= 39',
    '2*x0 + 2*x1 <= 71',
    '3*x0 - 3*x1 >= 0'
  ]
}
```

### Gurobi Code Implementation
To implement this problem in Gurobi, we use Python. Ensure you have Gurobi installed (`pip install gurobipy`) and a valid license.

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="mechanical_pencils")
x1 = m.addVar(vtype=GRB.INTEGER, name="headsets")

# Objective function: Minimize 1.36*x0 + 8.25*x1
m.setObjective(1.36*x0 + 8.25*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(9*x0 + 8*x1 >= 44, name="min_employee_satisfaction")
m.addConstr(9*x0 + 8*x1 <= 48, name="max_employee_satisfaction")
m.addConstr(5*x0 + 8*x1 >= 11, name="min_usefulness_rating")
m.addConstr(5*x0 + 8*x1 <= 25, name="max_usefulness_rating")
m.addConstr(2*x0 + 2*x1 >= 39, name="min_storage_space")
m.addConstr(2*x0 + 2*x1 <= 71, name="max_storage_space")
m.addConstr(3*x0 - 3*x1 >= 0, name="additional_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Mechanical Pencils: {x0.x}")
    print(f"Headsets: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```