To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical formulation that can be represented in code. The problem involves minimizing an objective function subject to several constraints, with variables representing quantities of 'CBRN platoons' and 'airborne infantry companies'. Each type of unit has attributes like offensive capability rating and deployment weight.

The objective function to minimize is: \(3 \times \text{quantity of CBRN platoons} + 6 \times \text{quantity of airborne infantry companies}\).

Constraints include:
- The total combined offensive capability rating must be at least 11.
- The minimum deployment weight for all units must be at least 12 metric tons.
- A specific linear combination of the quantities of CBRN platoons and airborne infantry companies must be greater than or equal to zero.
- The maximum total combined offensive capability rating is 36.
- The maximum total combined deployment weight is 27 metric tons.
- Both variables (quantities of units) are restricted to whole numbers.

Here's how we can translate these requirements into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the decision variables
CBRN_platoons = m.addVar(vtype=GRB.INTEGER, name="CBRN_platoons")
airborne_infantry_companies = m.addVar(vtype=GRB.INTEGER, name="airborne_infantry_companies")

# Objective function: Minimize 3*CBRN_platoons + 6*airborne_infantry_companies
m.setObjective(3*CBRN_platoons + 6*airborne_infantry_companies, GRB.MINIMIZE)

# Constraints
# Total combined offensive capability rating must be at least 11
m.addConstr(17*CBRN_platoons + 16*airborne_infantry_companies >= 11, name="Offensive Capability Min")

# Minimum deployment weight for all units is at least 12 metric tons
m.addConstr(2*CBRN_platoons + 17*airborne_infantry_companies >= 12, name="Deployment Weight Min")

# Specific linear combination of quantities must be greater than or equal to zero
m.addConstr(2*CBRN_platoons - 8*airborne_infantry_companies >= 0, name="Linear Combination Constraint")

# Maximum total combined offensive capability rating is 36
m.addConstr(17*CBRN_platoons + 16*airborne_infantry_companies <= 36, name="Offensive Capability Max")

# Maximum total combined deployment weight is 27 metric tons
m.addConstr(2*CBRN_platoons + 17*airborne_infantry_companies <= 27, name="Deployment Weight Max")

# Solve the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"CBRN platoons: {CBRN_platoons.x}")
    print(f"Airborne infantry companies: {airborne_infantry_companies.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```