To solve the given optimization problem using Gurobi, we need to define the variables, objective function, and constraints according to the provided specifications. The problem involves minimizing an objective function that is a linear combination of the quantities of yellow highlighters, paper clips, packs of paper, and manila envelopes, subject to various constraints related to workplace safety impact and employee satisfaction impact.

The decision variables are:
- `x0`: Quantity of yellow highlighters
- `x1`: Quantity of paper clips
- `x2`: Quantity of packs of paper
- `x3`: Quantity of manila envelopes

The objective function to minimize is: `5*x0 + 8*x1 + 4*x2 + 2*x3`

Constraints are defined based on the workplace safety impact and employee satisfaction impact of each item, as well as their combinations.

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="yellow_highlighters")
x1 = m.addVar(vtype=GRB.INTEGER, name="paper_clips")
x2 = m.addVar(vtype=GRB.INTEGER, name="packs_of_paper")
x3 = m.addVar(vtype=GRB.INTEGER, name="manila_envelopes")

# Objective function
m.setObjective(5*x0 + 8*x1 + 4*x2 + 2*x3, GRB.MINIMIZE)

# Constraints
# Workplace safety impact constraints
m.addConstr(9*x0 + 6*x1 >= 17, name="workplace_safety_1")
m.addConstr(6*x1 + 7*x2 >= 30, name="workplace_safety_2")
m.addConstr(9*x0 + 7*x2 >= 25, name="workplace_safety_3")
m.addConstr(6*x1 + 5*x3 >= 23, name="workplace_safety_4")
m.addConstr(9*x0 + 6*x1 + 7*x2 + 5*x3 >= 25, name="workplace_safety_total")

# Employee satisfaction impact constraints
m.addConstr(8*x0 + 10*x1 >= 15, name="employee_satisfaction_1")
m.addConstr(10*x1 + 2*x2 >= 16, name="employee_satisfaction_2")
m.addConstr(2*x2 + 8*x3 >= 10, name="employee_satisfaction_3")
m.addConstr(10*x1 + 8*x3 >= 14, name="employee_satisfaction_4")
m.addConstr(8*x0 + 2*x2 >= 24, name="employee_satisfaction_5")
m.addConstr(8*x0 + 10*x1 + 2*x2 + 8*x3 >= 24, name="employee_satisfaction_total")

# Additional constraints
m.addConstr(3*x1 - x2 >= 0, name="additional_constraint_1")
m.addConstr(4*x0 - x1 >= 0, name="additional_constraint_2")
m.addConstr(6*x1 + 7*x2 <= 105, name="workplace_safety_limit_1")
m.addConstr(6*x1 + 7*x2 + 5*x3 <= 126, name="workplace_safety_limit_2")
m.addConstr(9*x0 + 6*x1 + 7*x2 <= 59, name="workplace_safety_limit_3")
m.addConstr(9*x0 + 7*x2 + 5*x3 <= 61, name="workplace_safety_limit_4")
m.addConstr(8*x0 + 8*x3 <= 70, name="employee_satisfaction_limit_1")
m.addConstr(8*x0 + 10*x1 + 2*x2 <= 72, name="employee_satisfaction_limit_2")
m.addConstr(10*x1 + 2*x2 + 8*x3 <= 79, name="employee_satisfaction_limit_3")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Yellow highlighters: {x0.x}")
    print(f"Paper clips: {x1.x}")
    print(f"Packs of paper: {x2.x}")
    print(f"Manila envelopes: {x3.x}")
else:
    print("No optimal solution found")
```
```python
```