## Step 1: Define the symbolic representation of the variables
The variables are 'blue highlighters', 'staplers', and 'cafeteria chairs'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $5x_1 + 8x_2 + 3x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $3.05x_1 + 0.11x_2 + 7.99x_3 \leq 67$ (storage space constraint for all items)
- $0.11x_2 + 7.99x_3 \leq 61$ (storage space constraint for staplers and cafeteria chairs)
- $3.05x_1 + 7.99x_3 \leq 22$ (storage space constraint for blue highlighters and cafeteria chairs)
- $x_1, x_2, x_3$ must be integers (integrality constraints)

## 4: Correcting and Clarifying Constraints
Upon closer inspection, it seems there might have been a misunderstanding in directly translating the constraints. The problem statement mentions:
- A maximum of 61 ft^2 of storage space can be used for staplers plus cafeteria chairs: $0.11x_2 + 7.99x_3 \leq 61$
- You're not allowed to use more than 22 square feet of storage space for blue highlighters plus cafeteria chairs: $3.05x_1 + 7.99x_3 \leq 22$
- Up to 57 square feet of storage space can be used for blue highlighters, staplers and cafeteria chairs: $3.05x_1 + 0.11x_2 + 7.99x_3 \leq 57$
- And implicitly, $3.05x_1 + 0.11x_2 + 7.99x_3 \leq 67$ seems to be an overall storage constraint.

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'blue highlighters'), ('x2', 'staplers'), ('x3', 'cafeteria chairs')],
'objective_function': '5*x1 + 8*x2 + 3*x3',
'constraints': [
    '3.05*x1 + 0.11*x2 + 7.99*x3 <= 67',
    '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 and x1 is an integer',
    'x2 >= 0 and x2 is an integer',
    'x3 >= 0 and x3 is an integer'
]
}
```

## 6: Gurobi Code
Now, let's implement this in Gurobi:
```python
import gurobi

# Create a new model
m = gurobi.Model()

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

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

# Constraints
m.addConstr(3.05*x1 + 0.11*x2 + 7.99*x3 <= 67)
m.addConstr(0.11*x2 + 7.99*x3 <= 61)
m.addConstr(3.05*x1 + 7.99*x3 <= 22)
m.addConstr(3.05*x1 + 0.11*x2 + 7.99*x3 <= 57)

# Optimize
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('Blue highlighters: ', x1.varValue)
    print('Staplers: ', x2.varValue)
    print('Cafeteria chairs: ', x3.varValue)
else:
    print('No optimal solution found')
```