To solve the given optimization problem, we will use Gurobi, a popular linear programming solver. The problem involves minimizing an objective function subject to several constraints.

The decision variables are:
- `x0`: quantity of office chairs
- `x1`: quantity of color printers

The objective function is: 
`7.49 * x0 + 9.65 * x1`

Constraints:
1. Storage space constraint for office chairs and color printers: `19 * x0 + 16 * x1 <= 71`
2. Cost constraint for office chairs and color printers: `8 * x0 + 21 * x1 <= 60`
3. Minimum storage usage constraint: `19 * x0 + 16 * x1 >= 25`
4. Minimum cost constraint: `8 * x0 + 21 * x1 >= 30`
5. Linear constraint: `5 * x0 - 7 * x1 >= 0`

Given the problem description, we need to ensure that both `x0` and `x1` are integers since they represent quantities of items.

Here is the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define decision variables as integers
x0 = m.addVar(vtype=GRB.INTEGER, name="office_chairs")
x1 = m.addVar(vtype=GRB.INTEGER, name="color_printers")

# Set the objective function
m.setObjective(7.49 * x0 + 9.65 * x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(19 * x0 + 16 * x1 <= 110, "storage_space_constraint")
m.addConstr(8 * x0 + 21 * x1 <= 79, "cost_constraint")
m.addConstr(19 * x0 + 16 * x1 >= 25, "minimum_storage_usage")
m.addConstr(8 * x0 + 21 * x1 >= 30, "minimum_cost")
m.addConstr(5 * x0 - 7 * x1 >= 0, "linear_constraint")

# Update the model
m.update()

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Quantity of office chairs: {x0.x}")
    print(f"Quantity of color printers: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```