To solve the optimization problem described, we need to translate the given objective function and constraints into a mathematical formulation that can be implemented in Gurobi. The objective is to maximize the function $5x_0 + 8x_1 + 3x_2$, where $x_0$ represents the number of blue highlighters, $x_1$ represents the quantity of staplers, and $x_2$ represents the quantity of cafeteria chairs.

The constraints given are:
1. Storage space for blue highlighters: $3.05x_0$
2. Storage space for staplers: $0.11x_1$
3. Storage space for cafeteria chairs: $7.99x_2$
4. Maximum storage space for staplers and cafeteria chairs: $0.11x_1 + 7.99x_2 \leq 61$
5. Maximum storage space for blue highlighters and cafeteria chairs: $3.05x_0 + 7.99x_2 \leq 22$
6. Total maximum storage space for all items: $3.05x_0 + 0.11x_1 + 7.99x_2 \leq 57$

Additionally, $x_0$, $x_1$, and $x_2$ must be integers.

Here is the Gurobi code that captures this optimization problem:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="blue_highlighters")
x1 = m.addVar(vtype=GRB.INTEGER, name="staplers")
x2 = m.addVar(vtype=GRB.INTEGER, name="cafeteria_chairs")

# Set the objective function
m.setObjective(5*x0 + 8*x1 + 3*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.11*x1 + 7.99*x2 <= 61, "staplers_and_chairs")
m.addConstr(3.05*x0 + 7.99*x2 <= 22, "highlighters_and_chairs")
m.addConstr(3.05*x0 + 0.11*x1 + 7.99*x2 <= 57, "total_storage")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Blue Highlighters: {x0.x}")
    print(f"Staplers: {x1.x}")
    print(f"Cafeteria Chairs: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```