## Step 1: Define the optimization problem
The problem is to maximize the objective function: $8.74x_1 + 6.8x_2 + 1.88x_3 + 8.47x_4 + 4.5x_5 + 6.83x_6$, subject to various constraints.

## Step 2: List all variables and their defensive capability ratings
Let $x_1$ be the number of air defense batteries, $x_2$ be the number of CBRN platoons, $x_3$ be the number of armored companies, $x_4$ be the number of water purification units, $x_5$ be the number of pathfinder teams, and $x_6$ be the number of reconnaissance troops.

The defensive capability ratings are:
- Air defense batteries: $5x_1$
- CBRN platoons: $4x_2$
- Armored companies: $8x_3$
- Water purification units: $4x_4$
- Pathfinder teams: $12x_5$
- Reconnaissance troops: $11x_6$

## Step 3: Formulate constraints
There are many constraints given, including:
- $5x_1 + 12x_5 \geq 20$
- $4x_2 + 4x_4 + 11x_6 \geq 23$
- $4x_4 + 12x_5 + 11x_6 \geq 23$
- ... (many more constraints)

## 4: Implement the optimization problem using Gurobi
We will use Gurobi to solve this optimization problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define variables
x1 = m.addVar(name="air_defense_batteries", vtype=gp.GRB_INTEGER)
x2 = m.addVar(name="CBRN_platoons", vtype=gp.GRB_INTEGER)
x3 = m.addVar(name="armored_companies", vtype=gp.GRB_INTEGER)
x4 = m.addVar(name="water_purification_units", vtype=gp.GRB_INTEGER)
x5 = m.addVar(name="pathfinder_teams", vtype=gp.GRB_INTEGER)
x6 = m.addVar(name="reconnaissance_troops", vtype=gp.GRB_INTEGER)

# Objective function
m.setObjective(8.74 * x1 + 6.8 * x2 + 1.88 * x3 + 8.47 * x4 + 4.5 * x5 + 6.83 * x6, gp.GRB_MAXIMIZE)

# Constraints
m.addConstr(5 * x1 + 12 * x5 >= 20)
m.addConstr(4 * x2 + 4 * x4 + 11 * x6 >= 23)
m.addConstr(4 * x4 + 12 * x5 + 11 * x6 >= 23)
# Add other constraints here...

# Add upper bound constraints
m.addConstr(8 * x3 + 4 * x4 <= 137)
m.addConstr(5 * x1 + 11 * x6 <= 110)
m.addConstr(4 * x4 + 12 * x5 <= 25)
m.addConstr(8 * x3 + 4 * x2 <= 117)
m.addConstr(8 * x3 + 11 * x6 <= 38)
m.addConstr(8 * x1 + 4 * x2 <= 112)
m.addConstr(8 * x3 + 12 * x5 <= 74)
m.addConstr(4 * x4 + 11 * x6 <= 32)
m.addConstr(5 * x1 + 12 * x5 <= 31)
m.addConstr(8 * x1 + 4 * x2 + 11 * x6 <= 114)
m.addConstr(4 * x2 + 4 * x4 + 12 * x5 <= 77)
m.addConstr(8 * x1 + 8 * x3 + 12 * x5 <= 90)
m.addConstr(5 * x1 + 4 * x2 + 8 * x3 + 4 * x4 + 12 * x5 + 11 * x6 <= 90)

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB_OPTIMAL:
    print("Optimal solution found.")
    print("Air defense batteries:", x1.varValue)
    print("CBRN platoons:", x2.varValue)
    print("Armored companies:", x3.varValue)
    print("Water purification units:", x4.varValue)
    print("Pathfinder teams:", x5.varValue)
    print("Reconnaissance troops:", x6.varValue)
    print("Objective function value:", m.objVal)
else:
    print("No optimal solution found.")
```