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

### Symbolic Representation

- **Variables**: Let `x1` represent the quantity of manila envelopes and `x2` represent the quantity of hole punches.
- **Objective Function**: The goal is to minimize `6.04*x1 + 3.72*x2`.
- **Constraints**:
  1. Cost constraint: `12*x1 + 14*x2 >= 26`
  2. Storage space constraint: `x1 + 14*x2 >= 9`
  3. Workplace safety impact constraint: `8*x1 + 8*x2 >= 39`
  4. Employee satisfaction impact constraint: `6*x1 + 4*x2 >= 38`
  5. Additional linear constraint: `5*x1 - 3*x2 >= 0`
  6. Budget constraint: `12*x1 + 14*x2 <= 55`
  7. Storage space upper bound: `x1 + 14*x2 <= 42`
  8. Workplace safety impact upper bound: `8*x1 + 8*x2 <= 43`
  9. Employee satisfaction impact upper bound: `6*x1 + 4*x2 <= 83`
  10. Non-negativity and integer constraints for both variables: `x1, x2 >= 0` and `x1, x2` are integers.

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'manila envelopes'), ('x2', 'hole punches')],
    'objective_function': '6.04*x1 + 3.72*x2',
    'constraints': [
        '12*x1 + 14*x2 >= 26',
        'x1 + 14*x2 >= 9',
        '8*x1 + 8*x2 >= 39',
        '6*x1 + 4*x2 >= 38',
        '5*x1 - 3*x2 >= 0',
        '12*x1 + 14*x2 <= 55',
        'x1 + 14*x2 <= 42',
        '8*x1 + 8*x2 <= 43',
        '6*x1 + 4*x2 <= 83'
    ]
}
```

### Gurobi Code

To solve this optimization problem using Gurobi, we can use the following Python code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="manila_envelopes")
x2 = m.addVar(vtype=GRB.INTEGER, name="hole_punches")

# Set objective function
m.setObjective(6.04*x1 + 3.72*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(12*x1 + 14*x2 >= 26, "cost_constraint")
m.addConstr(x1 + 14*x2 >= 9, "storage_space_lower_bound")
m.addConstr(8*x1 + 8*x2 >= 39, "workplace_safety_impact_lower_bound")
m.addConstr(6*x1 + 4*x2 >= 38, "employee_satisfaction_impact_lower_bound")
m.addConstr(5*x1 - 3*x2 >= 0, "additional_linear_constraint")
m.addConstr(12*x1 + 14*x2 <= 55, "budget_upper_bound")
m.addConstr(x1 + 14*x2 <= 42, "storage_space_upper_bound")
m.addConstr(8*x1 + 8*x2 <= 43, "workplace_safety_impact_upper_bound")
m.addConstr(6*x1 + 4*x2 <= 83, "employee_satisfaction_impact_upper_bound")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Manila envelopes: {x1.x}")
    print(f"Hole punches: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```