## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'office chairs' and 'color printers'. Let's denote 'office chairs' as $x_1$ and 'color printers' as $x_2$. The objective function to minimize is $7.49x_1 + 9.65x_2$. The constraints are:
- $19x_1 + 16x_2 \geq 25$ (storage space usage)
- $8x_1 + 21x_2 \geq 30$ (dollar cost)
- $5x_1 - 7x_2 \geq 0$ (linear constraint)
- $19x_1 + 16x_2 \leq 71$ (storage space upper limit)
- $8x_1 + 21x_2 \leq 60$ (dollar cost upper limit)
- $x_1, x_2 \geq 0$ and are integers.

## Step 2: Convert the problem into a symbolic representation
The symbolic variables are:
- $x_1$ for 'office chairs'
- $x_2$ for 'color printers'

The objective function is: $7.49x_1 + 9.65x_2$

The constraints are:
- $19x_1 + 16x_2 \geq 25$
- $8x_1 + 21x_2 \geq 30$
- $5x_1 - 7x_2 \geq 0$
- $19x_1 + 16x_2 \leq 71$
- $8x_1 + 21x_2 \leq 60$
- $x_1, x_2$ are integers and $x_1, x_2 \geq 0$

## 3: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'office chairs'), ('x2', 'color printers')],
    'objective_function': '7.49*x1 + 9.65*x2',
    'constraints': [
        '19*x1 + 16*x2 >= 25',
        '8*x1 + 21*x2 >= 30',
        '5*x1 - 7*x2 >= 0',
        '19*x1 + 16*x2 <= 71',
        '8*x1 + 21*x2 <= 60',
        'x1, x2 are integers and x1, x2 >= 0'
    ]
}
```

## 4: Implement the problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(vtype=gurobi.GRB.INTEGER, name="office_chairs")
    x2 = model.addVar(vtype=gurobi.GRB.INTEGER, name="color_printers")

    # Objective function
    model.setObjective(7.49 * x1 + 9.65 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(19 * x1 + 16 * x2 >= 25, name="storage_space_min")
    model.addConstr(8 * x1 + 21 * x2 >= 30, name="dollar_cost_min")
    model.addConstr(5 * x1 - 7 * x2 >= 0, name="linear_constraint")
    model.addConstr(19 * x1 + 16 * x2 <= 71, name="storage_space_max")
    model.addConstr(8 * x1 + 21 * x2 <= 60, name="dollar_cost_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Office chairs: {x1.varValue}")
        print(f"Color printers: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```