## Step 1: Define the symbolic representation of the variables
The variables are ['airborne infantry companies', 'reconnaissance troops', 'medical platoons', 'military intelligence companies']. Let's denote them as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic variables
The symbolic variables are: 
- $x_1$ for 'airborne infantry companies'
- $x_2$ for 'reconnaissance troops'
- $x_3$ for 'medical platoons'
- $x_4$ for 'military intelligence companies'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8.45x_1 + 6.05x_2 + 1.68x_3 + 9.56x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $4.55x_1 + 1.03x_3 \geq 19$
- $9.92x_2 + 8.91x_4 \geq 14$
- $9.92x_2 + 1.03x_3 \geq 29$
- $4.55x_1 + 8.91x_4 \geq 22$
- $1.03x_3 + 8.91x_4 \geq 25$
- $9.92x_2 + 1.03x_3 + 8.91x_4 \geq 32$
- $9.92x_2 + 1.03x_3 \leq 71$
- $4.55x_1 + 8.91x_4 \leq 37$
- $1.03x_3 + 8.91x_4 \leq 129$
- $4.55x_1 + 1.03x_3 + 8.91x_4 \leq 68$
- $4.55x_1 + 9.92x_2 + 1.03x_3 + 8.91x_4 \leq 68$

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'airborne infantry companies'), 
        ('x2', 'reconnaissance troops'), 
        ('x3', 'medical platoons'), 
        ('x4', 'military intelligence companies')
    ], 
    'objective_function': '8.45*x1 + 6.05*x2 + 1.68*x3 + 9.56*x4', 
    'constraints': [
        '4.55*x1 + 1.03*x3 >= 19',
        '9.92*x2 + 8.91*x4 >= 14',
        '9.92*x2 + 1.03*x3 >= 29',
        '4.55*x1 + 8.91*x4 >= 22',
        '1.03*x3 + 8.91*x4 >= 25',
        '9.92*x2 + 1.03*x3 + 8.91*x4 >= 32',
        '9.92*x2 + 1.03*x3 <= 71',
        '4.55*x1 + 8.91*x4 <= 37',
        '1.03*x3 + 8.91*x4 <= 129',
        '4.55*x1 + 1.03*x3 + 8.91*x4 <= 68',
        '4.55*x1 + 9.92*x2 + 1.03*x3 + 8.91*x4 <= 68'
    ]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name='airborne_infantry_companies', vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name='reconnaissance_troops', vtype=gurobi.GRB.INTEGER)
x3 = model.addVar(name='medical_platoons', vtype=gurobi.GRB.INTEGER)
x4 = model.addVar(name='military_intelligence_companies', vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(8.45 * x1 + 6.05 * x2 + 1.68 * x3 + 9.56 * x4, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(4.55 * x1 + 1.03 * x3 >= 19)
model.addConstr(9.92 * x2 + 8.91 * x4 >= 14)
model.addConstr(9.92 * x2 + 1.03 * x3 >= 29)
model.addConstr(4.55 * x1 + 8.91 * x4 >= 22)
model.addConstr(1.03 * x3 + 8.91 * x4 >= 25)
model.addConstr(9.92 * x2 + 1.03 * x3 + 8.91 * x4 >= 32)
model.addConstr(9.92 * x2 + 1.03 * x3 <= 71)
model.addConstr(4.55 * x1 + 8.91 * x4 <= 37)
model.addConstr(1.03 * x3 + 8.91 * x4 <= 129)
model.addConstr(4.55 * x1 + 1.03 * x3 + 8.91 * x4 <= 68)
model.addConstr(4.55 * x1 + 9.92 * x2 + 1.03 * x3 + 8.91 * x4 <= 68)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Airborne Infantry Companies: ', x1.varValue)
    print('Reconnaissance Troops: ', x2.varValue)
    print('Medical Platoons: ', x3.varValue)
    print('Military Intelligence Companies: ', x4.varValue)
else:
    print('The model is infeasible')
```