To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (blue highlighters, staplers, cafeteria chairs), formulating the objective function with these variables, and expressing the constraints using these variables.

Let's denote:
- \(x_1\) as the number of blue highlighters,
- \(x_2\) as the number of staplers,
- \(x_3\) as the number of cafeteria chairs.

The objective function to maximize is given by: \(5x_1 + 8x_2 + 3x_3\).

The constraints are:
1. Storage space for blue highlighters: \(3.05x_1\)
2. Storage space for staplers: \(0.11x_2\)
3. Storage space for cafeteria chairs: \(7.99x_3\)
4. Maximum storage space for staplers plus cafeteria chairs: \(0.11x_2 + 7.99x_3 \leq 61\)
5. Maximum storage space for blue highlighters plus cafeteria chairs: \(3.05x_1 + 7.99x_3 \leq 22\)
6. Total maximum storage space for all items: \(3.05x_1 + 0.11x_2 + 7.99x_3 \leq 57\)
7. Non-negativity and integer constraints for all variables: \(x_1, x_2, x_3 \geq 0\) and \(x_1, x_2, x_3 \in \mathbb{Z}\)

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'blue highlighters'), ('x2', 'staplers'), ('x3', 'cafeteria chairs')],
  'objective_function': '5*x1 + 8*x2 + 3*x3',
  'constraints': [
    '3.05*x1 <= 57', 
    '0.11*x2 + 7.99*x3 <= 61', 
    '3.05*x1 + 7.99*x3 <= 22', 
    '3.05*x1 + 0.11*x2 + 7.99*x3 <= 57',
    'x1 >= 0', 'x2 >= 0', 'x3 >= 0',
    'x1 == int(x1)', 'x2 == int(x2)', 'x3 == int(x3)'
  ]
}
```

To solve this problem using Gurobi, we will write the following Python code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="blue_highlighters")
x2 = m.addVar(vtype=GRB.INTEGER, name="staplers")
x3 = m.addVar(vtype=GRB.INTEGER, name="cafeteria_chairs")

# Set objective function
m.setObjective(5*x1 + 8*x2 + 3*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.11*x2 + 7.99*x3 <= 61, "staplers_and_chairs")
m.addConstr(3.05*x1 + 7.99*x3 <= 22, "highlighters_and_chairs")
m.addConstr(3.05*x1 + 0.11*x2 + 7.99*x3 <= 57, "total_storage")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Blue Highlighters:", x1.x)
    print("Staplers:", x2.x)
    print("Cafeteria Chairs:", x3.x)
    print("Objective Value:", m.objVal)
else:
    print("No optimal solution found")
```